視覺質量評估在各種多媒體應用中起到了關鍵性的作用。因為人類的視覺系統是視覺信號的最終接收髓，王觀視覺質量評估被認為是最可靠的視覺質量評估方法。然而，王觀視覺質量評估耗時、昂貴，並且不適合線上應用。因此，自動的、客觀的視覺質量評估方法已經被開發並被應用於很多實用埸合當中。最廣泛使用的客觀視覺質量度量方法，如均方差(MSE) 、峰值信噪比(PSNR) 等與人IN對視覺信號質量的判斷相距甚遠。因此，開發更準確的客觀質量度量算法將會成為未來視覺信號處理和傳輸應用成功與否的重要因素。 / 該論文主要研究全參考客觀視覺質量度量算法。主要內容分為三部分。 / 第一部分討論圖像質量評估。首先研究了一個經典的圖像質量度量算法--SSIM。提出了個新的加權方法並整合至IjSSIM 當中，提升了SSIM自可預測精度。之後，受到前面這個工作的故發，設計7 個全新的圖像質量度量算法，將噪聲分類為加性噪聲和細節失兩大類。這個算法在很多主觀質量圓像資料庫上都有很優秀的預測表現。 / 第二部分研究視頻質量評估。首先，將上面提到的全新的圓像質量度量算法通過挖掘視頻運動信息和時域相關的人眼視覺特性擴展為視頻質量度量算法。方法包括:使用基於人自民運動的時空域對比敏感度方程，使用基於運動崗量的時域視覺掩蓋，使用基於認知層面的空域整合等等。這個算法被證明對處理標清和高清序列同樣有效。其次，提出了一個測量視頻順間不一致程度的算法。該算法被整合到MSE 中，提高了MSE的預測表現。 / 上面提到的算法只考慮到了亮度噪聲。論文的最後部分通過個具體應用色差立體圓像生成究了色度噪聲。色差立體圖像是三維立體顯示技衛的其中種方法。它使在普通電視、電腦顯示器、甚至印刷品上顯示三維立體效果成為可能。我們提出了一個新的色差立體圖像生成方法。該方法工作在CIELAB彩色空間，並力圖匹配原始圖像與觀測立體圖像的色彩屬性值。 / Visual quality assessment (VQA) plays a fundamental role in multimedia applications. Since the human visual system (HVS) is the ultimate viewer of the visual information, subjective VQA is considered to be the most reliable way to evaluate visual quality. However, subjective VQA is time-consuming, expensive, and not feasible for on-line manipulation. Therefore, automatic objective VQA algorithms, or namely visual quality metrics, have been developed and widely used in practical applications. However, it is well known that the popular visual quality metrics, such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), etc., correlate poorly with the human perception of visual quality. The development of more accurate objective VQA algorithms becomes of paramount importance to the future visual information processing and communication applications. / In this thesis, full-reference objective VQA algorithms are investigated. Three parts of the work are discussed as briefly summarized below. / The first part concerns image quality assessment. It starts with the investigation of a popular image quality metric, i.e., Structural Similarity Index (SSIM). A novel weighting function is proposed and incorporated into SSIM, which leads to a substantial performance improvement in terms of matching subjective ratings. Inspired by this work, a novel image quality metric is developed by separately evaluating two distinct types of spatial distortions: detail losses and additive impairments. The pro- posed method demonstrates the state-of-the-art predictive performance on most of the publicly-available subjective quality image databases. / The second part investigates video quality assessment. We extend the proposed image quality metric to assess video quality by exploiting motion information and temporal HVS characteristics, e.g., eye movement spatio-velocity contrast sensitivity function, temporal masking using motion vectors, temporal pooling considering human cognitive behaviors, etc. It has been experimentally verified that the proposed video quality metric can achieve good performance on both standard-definition and high-definition video databases. We also propose a novel method to measure temporal inconsistency, an essential type of video temporal distortions. It is incorporated into the MSE for video quality assessment, and experiments show that it can significantly enhance MSE's predictive performance. / The aforementioned algorithms only analyze luminance distortions. In the last part, we investigate chrominance distortions for a specific application: anaglyph image generation. Anaglyph image is one of the 3D displaying techniques, which enables stereoscopic perception on traditional TVs, PC monitors, projectors, and even papers. Three perceptual color attributes are taken into account for the color distortion measure, i.e., lightness, saturation, and hue, based on which a novel anaglyph image generation algorithm is developed via approximation in the CIELAB color space. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Li, Songnan. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 122-130). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / Dedication --- p.ii / Acknowledgments --- p.iii / Abstract --- p.vi / Publications --- p.viii / Nomenclature --- p.xii / Contents --- p.xvii / List of Figures --- p.xx / List of Tables --- p.xxii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation and Objectives --- p.1 / Chapter 1.2 --- Overview of Subjective Visual Quality Assessment --- p.3 / Chapter 1.2.1 --- Viewing condition --- p.4 / Chapter 1.2.2 --- Candidate observer selection --- p.4 / Chapter 1.2.3 --- Test sequence selection --- p.4 / Chapter 1.2.4 --- Structure of test session --- p.5 / Chapter 1.2.5 --- Assessment procedure --- p.6 / Chapter 1.2.6 --- Post-processing of scores --- p.7 / Chapter 1.3 --- Overview of Objective Visual Quality Assessment --- p.8 / Chapter 1.3.1 --- Classification --- p.8 / Chapter 1.3.2 --- HVS-model-based metrics --- p.9 / Chapter 1.3.3 --- Engineering-based metrics --- p.21 / Chapter 1.3.4 --- Performance evaluation method --- p.28 / Chapter 1.4 --- Thesis Outline --- p.29 / Chapter I --- Image Quality Assessment --- p.32 / Chapter 2 --- Weighted Structural Similarity Index based on Local Smoothness --- p.33 / Chapter 2.1 --- Introduction --- p.33 / Chapter 2.2 --- The Structural Similarity Index --- p.33 / Chapter 2.3 --- Influence of the Smooth Region on SSIM --- p.35 / Chapter 2.3.1 --- Overall performance analysis --- p.35 / Chapter 2.3.2 --- Performance analysis for individual distortion types --- p.37 / Chapter 2.4 --- The Proposed Weighted-SSIM --- p.40 / Chapter 2.5 --- Experiments --- p.41 / Chapter 2.6 --- Summary --- p.43 / Chapter 3 --- Image Quality Assessment by Decoupling Detail Losses and Additive Impairments --- p.44 / Chapter 3.1 --- Introduction --- p.44 / Chapter 3.2 --- Motivation --- p.45 / Chapter 3.3 --- Related Works --- p.47 / Chapter 3.4 --- The Proposed Method --- p.48 / Chapter 3.4.1 --- Decoupling additive impairments and useful image contents --- p.48 / Chapter 3.4.2 --- Simulating the HVS processing --- p.56 / Chapter 3.4.3 --- Two quality measures and their combination --- p.58 / Chapter 3.5 --- Experiments --- p.59 / Chapter 3.5.1 --- Subjective quality image databases --- p.59 / Chapter 3.5.2 --- Parameterization --- p.60 / Chapter 3.5.3 --- Overall performance --- p.61 / Chapter 3.5.4 --- Statistical significance --- p.62 / Chapter 3.5.5 --- Performance on individual distortion types --- p.64 / Chapter 3.5.6 --- Hypotheses validation --- p.66 / Chapter 3.5.7 --- Complexity analysis --- p.69 / Chapter 3.6 --- Summary --- p.70 / Chapter II --- Video Quality Assessment --- p.71 / Chapter 4 --- Video Quality Assessment by Decoupling Detail Losses and Additive Impairments --- p.72 / Chapter 4.1 --- Introduction --- p.72 / Chapter 4.2 --- Related Works --- p.73 / Chapter 4.3 --- The Proposed Method --- p.74 / Chapter 4.3.1 --- Framework --- p.74 / Chapter 4.3.2 --- Decoupling additive impairments and useful image contents --- p.75 / Chapter 4.3.3 --- Motion estimation --- p.76 / Chapter 4.3.4 --- Spatio-velocity contrast sensitivity function --- p.77 / Chapter 4.3.5 --- Spatial and temporal masking --- p.79 / Chapter 4.3.6 --- Two quality measures and their combination --- p.80 / Chapter 4.3.7 --- Temporal pooling --- p.81 / Chapter 4.4 --- Experiments --- p.82 / Chapter 4.4.1 --- Subjective quality video databases --- p.82 / Chapter 4.4.2 --- Parameterization --- p.83 / Chapter 4.4.3 --- With/without decoupling --- p.84 / Chapter 4.4.4 --- Overall predictive performance --- p.85 / Chapter 4.4.5 --- Performance on individual distortion types --- p.88 / Chapter 4.4.6 --- Cross-distortion performance evaluation --- p.89 / Chapter 4.5 --- Summary --- p.91 / Chapter 5 --- Temporal Inconsistency Measure --- p.92 / Chapter 5.1 --- Introduction --- p.92 / Chapter 5.2 --- The Proposed Method --- p.93 / Chapter 5.2.1 --- Implementation --- p.93 / Chapter 5.2.2 --- MSE TIM --- p.94 / Chapter 5.3 --- Experiments --- p.96 / Chapter 5.4 --- Summary --- p.97 / Chapter III --- Application related to Color and 3D Perception --- p.98 / Chapter 6 --- Anaglyph Image Generation --- p.99 / Chapter 6.1 --- Introduction --- p.99 / Chapter 6.2 --- Anaglyph Image Artifacts --- p.99 / Chapter 6.3 --- Related Works --- p.101 / Chapter 6.3.1 --- Simple anaglyphs --- p.101 / Chapter 6.3.2 --- XYZ and LAB anaglyphs --- p.102 / Chapter 6.3.3 --- Ghosting reduction methods --- p.103 / Chapter 6.4 --- The Proposed Method --- p.104 / Chapter 6.4.1 --- Gamma transfer --- p.104 / Chapter 6.4.2 --- Converting RGB to CIELAB --- p.105 / Chapter 6.4.3 --- Matching color appearance attributes in CIELAB color space --- p.106 / Chapter 6.4.4 --- Converting CIELAB to RGB --- p.110 / Chapter 6.4.5 --- Parameterization --- p.111 / Chapter 6.5 --- Experiments --- p.112 / Chapter 6.5.1 --- Subjective tests --- p.112 / Chapter 6.5.2 --- Results and analysis --- p.113 / Chapter 6.5.3 --- Complexity --- p.115 / Chapter 6.6 --- Summary --- p.115 / Chapter 7 --- Conclusions --- p.117 / Chapter 7.1 --- Contributions of the Thesis --- p.117 / Chapter 7.2 --- Future Research Directions --- p.120 / Bibliography --- p.122

Active vision systems are about mobile platform equipped with one or more than one cameras. They perceive what happens in their surroundings from the image streams the cameras grab. Such systems have a few fundamental tasks to tackle---they need to determine from time to time what their motion in space is, and should they have multiple cameras, they need to know how the cameras are relatively positioned so that visual information collected by the respective cameras can be related. In the simplest form, the tasks are about finding the motion of a camera, and finding the relative geometry of every two cameras, from the image streams the cameras collect. / On determining the ego-motion of a camera, there have been many previous works as well. However, again, most of the works require to track distinct features in the image stream or to infer the full optical flow field from the normal flow field. Different from the traditional works, utilizing no motion correspondence nor the epipolar geometry, a new method is developed that operates again on the normal flow data directly. The method has a number of features. It can employ the use of every normal flow data, thus requiring less texture from the image scene. A novel formulation of what the normal flow direction at an image position has to offer on the camera motion is given, and this formulation allows a locus of the possible camera motion be outlined from every data point. With enough data points or normal flows over the image domain, a simple voting scheme would allow the various loci intersect and pinpoint the camera motion. / On determining the relative geometry of two cameras, there already exist a number of calibration techniques in the literature. They are based on the presence of either some specific calibration objects in the imaged scene, or a portion of the scene that is observable by both cameras. However, in active vision, because of the "active" nature of the cameras, it could happen that a camera pair do not share much or anything in common in their visual fields. In the first part of this thesis, we propose a new solution method to the problem. The method demands image data under a rigid motion of the camera pair, but unlike the existing motion correspondence-based calibration methods it does not estimate the optical flows or motion correspondences explicitly. Instead it estimates the inter-camera geometry from the monocular normal flows. Moreover, we propose a strategy on selecting optimal groups of normal flow vectors to improve the accuracy and efficiency of the estimation. / The relative motion between a camera and the imaged environment generally induces a flow field in the image stream captured by the camera. The flow field, which is about motion correspondences of the various image positions over the image frames, is referred to as the optical flows in the literature. If the optical flow field of every camera can be made available, the motion of a camera can be readily determined, and so can the relative geometry of two cameras. However, due to the well-known aperture problem, directly observable at any image position is generally not the full optical flow, but only the component of it that is normal to the iso-brightness contour of the intensity profile at the position. The component is widely referred to as the normal flow. It is not impossible to infer the full flow field from the normal flow field, but then it requires some specific assumptions about the imaged scene, like it is smooth almost everywhere etc. / This thesis aims at exploring how the above two fundamental tasks can be tackled by operating on the normal flow field directly. The objective is, without the full flow inferred explicitly in the process, and in turn no specific assumption made about the imaged scene, the developed methods can be applicable to a wider set of scenes. The thesis consists of two parts. The first part is about how the inter-camera geometry of two cameras can be determined from the two monocular normal flow fields. The second part is about how a camera's ego-motion can be determined by examining only the normal flows the camera observes. / We have tested the methods on both synthetic image data and real image sequences. Experimental results show that the developed methods are effective in determining inter-camera geometry and camera motion from normal flow fields. / Yuan, Ding. / Adviser: Ronald Chung. / Source: Dissertation Abstracts International, Volume: 70-09, Section: B, page: . / Thesis submitted in: October 2008. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 121-131). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Charge-Coupled-Device (CCD) cameras have opened a new world in astronomy and other related sciences with their high quantum efficiency, stability, linearity, and easy handling. Nevertheless, there is still noise in raw CCD images and even more noise is added through the image calibration process. This makes it essential to know exactly how the calibration process impacts the noise level in the image. The properties and characteristics of the calibration frames were explored. This was done for bias frames, dark frames and flat-field frames at different temperatures and for different exposure times. At first, it seemed advantageous to scale down a dark frame from a high temperature to the temperature at which the image is taken. However, the different pixel populations have different doubling temperatures. Although the main population could be scaled down accurately, the hot pixel populations could not. A global doubling temperature cannot be used to scale down dark frames taken at one temperature to calibrate the image taken at another temperature. It was discovered that the dark count increased if the chip was exposed to light prior to measurements of the dark count. This increase, denoted as dark offset, is dependent on the time and intensity of the prior exposure of the chip to light. The dark offset decayes with a characteristic time constant of 50 seconds. The cause might be due to storage effects within chip. It was found that the standard procedures for image calibration did not always generate the best and fastest way to process an image with a high signal-to-noise ratio. This was shown for both master dark frames and master flat-field frames. In a real world example, possible night sessions using master frame calibration are explained. Three sessions are discussed in detail concerning the trade-offs in imaging time, memory requirements, calibration time, and noise level. An efficient method for obtaining a noise map of an image was developed, i.e., a method for determining how accurate single pixel values are, by approximating the noise in several different cases.

Advances in medical imaging technology have led to the acquisition of large number of images in different modalities. On some of these images the boundaries of key organs need to be accurately identified for treatment planning and diagnosis. This is typically performed manually by a physician who uses prior knowledge of organ shapes and locations to demarcate the boundaries of organs. Such manual segmentation is subjective, time consuming and prone to inconsistency. Automating this task has been found to be very challenging due to poor tissue contrast and ill-defined organ/tissue boundaries. This dissertation presents a genetic algorithm for combining representations of learned information such as known shapes, regional properties and relative location of objects into a single framework in order to perform automated segmentation. The algorithm has been tested on two different datasets: for segmenting hands on thermographic images and for prostate segmentation on pelvic computed tomography (CT) and magnetic resonance (MR) images. In this dissertation we report the results of segmentation in two dimensions (2D) for thermographic images; and two as well as three dimensions (3D) for pelvic images. We show that combining multiple features for segmentation improves segmentation accuracy as compared with segmentation using single features such as texture or shape alone.

Whilst the underlying mechanisms of atmospheric turbulence are complex, the observed effects on imaging can be described in simpler terms. In this thesis, I address the effects seen as geometric distortions in anisoplanatic imaging and propose new digital restorations techniques that are real-time capable and predictive. The anisoplanatic problem arises in wide-field telescopic imaging and in new ventures of astronomy such as giant telescopes that process wide-field imagery. The methods proposed here, both digital and digital-optical hybrid, remove the position dependent distortions as a precursor to image analysis. Previous existing digital restoration techniques have used a prototype formed by averaging an image time sequence for image registration where valuable high frequencies information is lost due to the low-pass filtering effect of averaging. The proposed techniques are capable of using any arbitrary frame in the sequence as prototype, thus circumventing the low pass filtering effect and also allowing real-time implementation. Furthermore, these techniques are made predictive by the use of Kalman filtering. The predictive capabilities of these techniques open a new path to the combination of digital processing and adaptive optics that can result in hybrid systems. The key to adoption of hybrid systems is to reduce the complexity and expense of the optics and couple this with digital processing prediction. To this end I also propose a new type of inexpensive and fast piezoelectric deformable mirror based on the vibration modes of circular PVDF membranes that exhibit striking similarities to Zernike polynomials. It requires only two electrodes for actuation and a very simple driving signal generator, therefore constituting an inexpensive and viable alternative to existing deformable mirrors. With the emergence of multi-conjugate adaptive optics (MCAO) and multiobject adaptive optics (MOAO) in astronomy, and the more demanding correction required for long range surveillance imaging, this inexpensive deformable mirror and the real-time capable digital algorithms are promising building blocks for a hybrid solution to the anisoplanatic imaging problem.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 107-117).

Thesis (Ph.D.)--University of Delaware, 2007. / Principal faculty advisor: Dennis W. Prather, Dept. of Electrical and Computer Engineering. Includes bibliographical references.

Thesis (M.S.)--Rochester Institute of Technology, 2007. / Typescript. Includes bibliographical references (leaves 76-77).

Thesis (M.S.)--Oregon State University, 2007. / Printout. Includes bibliographical references (leaves 48-51). Also available on the World Wide Web.

Hoy en día, los sistemas de imagen basados en cámaras CCD son ampliamente utilizados en numerosos campos, en particular, en el campo de la imagen científica debido a su alta resolución, alta eficiencia cuántica, amplia respuesta espectral, aceptable razón señal-ruido, linealidad, fidelidad geométrica, rápida respuesta, tamaño reducido y durabilidad.A pesar de esto, si se quiere utilizar una cámara CCD como instrumento de medida, se debe tener en cuenta que las cámaras CCD no son detectores perfectos, si no que presentan diversas fuentes de ruido inherentes a su funcionamiento que alteran los niveles digitales correspondientes a cada píxel, distorsionan la imagen real adquirida de forma desconocida y reducen la precisión radiométrica, la calidad de la imagen y su resolución.Dos de las relativamente recientes aplicaciones de los sistemas de imagen basados en cámaras CCD son la medida del color, consistente, básicamente, en estimar los valores triestímulo XYZ asociados a una muestra de color a partir de los niveles digitales de respuesta del sistema, y la reconstrucción espectral, consistente en estimar el espectro de reflectancia de una muestra de color a partir de los niveles digitales correspondientes de la respuesta del sistema.No obstante, para llevar a cabo medidas de color o reconstrucciones espectrales mediante este tipo de dispositivos es necesario realizar una caracterización o calibración previa de estos sistemas de imagen, con el objetivo de determinar la transformación que define la correspondencia entre las repuestas digitales del sistema y, por un lado, un espacio de color independiente del dispositivo, como el XYZ o el CIELAB, ya que las respuestas digitales del sistema, incluso las señales de salida RGB de un sistema de imagen tricromático, no se corresponden con los valores triestímulo independientes del dispositivo basados en el observador colorimétrico estándar de la CIE, o bien, por otro lado, el espacio de reflectancias espectrales, respectivamente.Los métodos de caracterización colorimétrica se pueden dividir en dos categorías generales: los métodos basados en las sensibilidades espectrales del sistema, algunos de los cuales normalmente sólo se aplican a configuraciones colorimétricas, es decir, con tres canales de adquisición, debido a su creciente complejidad al incrementar el numero de canales de adquisición, y los métodos basados en una carta de colores. Los métodos basados en las sensibilidades espectrales del sistema requieren el conocimiento de dichas sensibilidades para cada canal de adquisición, las cuales deben haberse determinado previamente mediante la caracterización espectral del sistema.En cuanto a los métodos de reconstrucción espectral, su principal objetivo es reconstruir el espectro de reflectancia, transmitancia o radiancia de una muestra de color a partir de las correspondientes respuestas digitales del sistema de imagen. Estos métodos se aplican habitualmente a configuraciones multiespectrales ya que los modelos lineales de espectros de reflectancia utilizados requieren como mínimo cuatro canales de adquisición para ser capaces de estimar espectros de reflectancia reales.Para que un sistema de imagen basado en una cámara CCD pueda ser utilizado como un instrumento de medida con elevada resolución espacial, de forma que la totalidad del área de detección del sistema sea útil para medir, es necesario corregir la no-uniformidad espacial de la respuesta del sistema. Con este propósito se utilizan básicamente dos tipos de técnicas. En primer lugar, las técnicas basadas en la escena se fundamentan en aplicar un algoritmo a la imagen original con el objetivo de obtener una mejora considerable en la calidad de la imagen a expensas de la precisión radiométrica. En segundo lugar, las técnicas de corrección de campo uniforme o de la nouniformidad espacial se basan en la calibración del sistema mediante dos imágenes: una imagen oscura y una imagen de campo uniforme, que se combinan linealmente con la imagen original. Este segundo tipo de técnicas permiten llevar a cabo medidas radiométricas precisas utilizando una cámara CCD. En la literatura se pueden encontrar diversas variantes de estas técnicas de corrección de campo uniforme o de la no-uniformidad espacial. La más general de estas variantes permite llevar a cabo la corrección de la no-uniformidad espacial de la respuesta del sistema de forma independiente de la nouniformidad de la iluminación de la escena, lo que resulta particularmente útil en varias condiciones de medida como, por ejemplo, en el caso de imágenes de objetos radiantes.La utilización de un sistema de imagen basado en una cámara CCD para medidas de color o reconstrucciones espectrales con elevada resolución espacial requiere la aplicación del segundo tipo de técnicas de corrección de la no-uniformidad espacial. En este trabajo se presentan la metodología experimental desarrollada para corregir las fuentes de ruido inherentes a un sistema de imagen basado en una cámara CCD, y la optimización de un algoritmo de corrección de la no-uniformidad espacial para obtener la mejor corrección posible de la no-uniformidad espacial.El principal objetivo de este trabajo es desarrollar un sistema de imagen multispectral para la medida del color. En este trabajo se presentan el diseño y desarrollo de un prototipo de sistema multiespectral en el rango visible del espectro y su minuciosa caracterización y análisis. Con este propósito se utiliza un sistema de imagen basado en una cámara CCD, por lo que es necesario llevar a cabo, en primer lugar, la corrección del ruido de la respuesta del sistema, concretamente la corrección de la no-uniformidad espacial, y, en segundo lugar, la caracterización o calibración del sistema mencionada anteriormente, para poder obtener los valores triestímulo XYZ y/o los espectros de reflectancia, respectivamente, a partir de las respuestas digitales del sistema. En este trabajo se utilizan dos sistemas de imagen basados en una cámara CCD: uno basado en una cámara CCD 10-bits color, y uno basado en una cámara CCD 12-bits monocromática refrigerada. De este último sistema se consideran dos configuraciones: una configuración colorimétrica con 3 canales de adquisición, y una configuración multiespectral con 7 canales de adquisición. La caracterización espectral se lleva a cabo sólo para la configuración colorimétrica de ambos sistemas con el objetivo de aplicar el método de caracterización colorimétrica basado en las sensibilidades espectrales del sistema. Por otro lado, se aplican diversos métodos de medida del color y reconstrucción espectral a las dos configuraciones del sistema basado en una cámara CCD 12-bits monocromática refrigerada y se comparan utilizando todas las combinaciones posibles de las cartas GretagMacbeth ColorChecker Color Rendition (CCCR) y GretagMacbeth ColorChecker DC (CCDC) como conjuntos de entrenamiento y prueba del sistema, con el objetivo de determinar los métodos más adecuados para cada configuración, es decir, los métodos que permiten conseguir la mejor precisión tanto en la medida del color como en la reconstrucción espectral para cada configuración. Al mismo tiempo se compara también el comportamiento de ambas configuraciones en términos de precisión de la medida del color y de la reconstrucción espectral.El hecho de que las sensibilidades espectrales de la mayoría de las cámaras CCD color (3 canales de adquisición) no verifiquen la condición de Luther, es decir, no sean transformaciones lineales de las funciones de igualación del color de la CIE, limita seriamente las aplicaciones colorimétricas de los sistemas basados en cámaras CCD color, dando lugar a valores triestímulo estimados dependientes del iluminante. Esta propiedad de las sensibilidades espectrales motiva el uso de sistemas multiespectrales ya que la única forma de asegurar una igualación del color para todos los observadores y bajo cambios en la iluminación es consiguiendo la igualación espectral. El método más directo para obtener información espectral de las muestras medidas es incrementar el muestreo por encima de los tres canales de adquisición tradicionales mediante filtros de banda estrecha, lo que se conoce como un sistema de imagen multiespectral. Los campos de aplicación de los sistemas deimagen multiespectral se ha incrementado enormemente en los últimos años, fundamentalmente debido a la posibilidad que ofrecen de estimar con precisión el espectro de reflectancia en cada píxel y, a partir de éste, los valores triestímulo XYZ, evitando del metamerismo.El sistema de imagen multiespectral diseñado y desarrollado en este trabajo doctoral para la medida del color está compuesto por un cámara CCD 12-bits monocromática refrigerada, una rueda de filtros motorizada y controlada vía software con un conjunto de filtros interferenciales de banda estrecha y un objetivo de focal variable. En coherencia con los resultados obtenidos en trabajos previos [Vilaseca et al., 2006] en la región NIR del espectro y extrapolándolos al rango visible, se utiliza un conjunto de siete filtros interferenciales de banda estrecha cubriendo por completo el rango visible del espectro, con la misma FWHM y longitudes de onda de pico equidistantes. Cada filtro constituye un canal de adquisición del sistema multiespectral, que corresponde a la configuración multiespectral del sistema de imagen antes mencionado.El primer paso antes de poder utilizar un sistema de imagen basado en una cámara CCD como instrumento de medida con elevada resolución espacial es llevar a cabo la corrección de las diferentes fuentes de ruido inherentes a su funcionamiento, y muy concretamente la corrección de la nouniformidad espacial de la respuesta del sensor. Con esta objetivo, en este trabajo se ha desarrollado una metodología experimental para la corrección de dichas fuentes de ruido, y se ha llevado a cabo la optimización de un algoritmo de corrección de la no-uniformidad espacial.A lo largo de este trabajo doctoral se han realizado también diversos análisis con el objetivo de mejorar la precisión de la medida del color y de la reconstrucción espectral utilizando sistemas de imagen basados en cámaras CCD.En primer lugar, considerando los conceptos básicos aplicados en imagen de alto rango dinámico (HDRI) para obtener una representación del contenido visual de una escena real independiente del dispositivo, se propone un balance de adaptación luminosa para incrementar el rango dinámico del sistema mediante la captura de imágenes con diferentes tiempos de exposición obteniendo así niveles digitales útiles para todos los píxeles. La aplicación de este balance de adaptación luminosa permite determinar el color en todos los píxeles de la imagen, incrementando así el rango dinámico del sistema [Pujol et al., 2006].En segundo lugar, se analiza la influencia del número de muestras del conjunto de entrenamiento en la precisión de la medida del color y la reconstrucción espectral con el objetivo de determinar si existe alguna relación entre la precisión y el tamaño del conjunto de entrenamiento. La precisión del sistema mejora incrementando el tamaño del conjunto de entrenamiento hasta alrededor de 110 muestras, y pasa a ser independientes del conjunto de entrenamiento utilizado para conjuntos de entrenamiento con un número de muestras igual o superior a 110.A continuación, se analizan la medida del color y la reconstrucción espectral llevadas a cabo utilizando las dos configuraciones del sistema, colorimétrica y multiespectral, en función de las gamas de colores medidas, es decir, conjuntos de muestras de color agrupadas en función de su tono, con el objetivo de determinar si estas configuraciones son especialmente sensibles a algunos tonos y/o a otras propiedades del color. En primer lugar se analizan las tendencias generales utilizando la carta CCDC como conjunto de entrenamiento y prueba y, en segundo lugar, se utilizan las 1269 muestras de color del Munsell Book of Color - Matte Collection, clasificadas en 10 tonos Munsell y cada uno de éstos en 4 sub-tonos, para analizar la influencia de la homogeneidad en tono del conjunto de entrenamiento.Se comprueba que la homogeneidad en tono del conjunto de entrenamiento permite mejorar de forma significativa la precisión del sistema tanto en la medida del color como en la reconstrucción espectral [de Lasarte et al., 2008 - 2]. Por otro lado, se utilizan tres combinaciones de conjuntos de entrenamiento y prueba de las muestras Munsell para variar el grado de homogeneidad en tono del conjunto de entrenamiento, obteniéndose los mejores resultados para los conjuntos de entrenamiento más homogéneos en tono.Los resultados obtenidos se analizan también en función de las características del color de las muestras medidas como son las coordenadas CIELAB, y las coordenadas Munsell de tono, 'value' y croma. No se observa ningún tipo de correlación entre la precisión del sistema y las coordenadas CIELAB, mientras que la precisión del sistema tiende a empeorar para muestras con valores de la coordenada Munsell Value V > 7 - 8.Se analiza también la influencia del iluminante mediante la comparación de los resultados obtenidos utilizando dos iluminantes: una lámpara incandescente y un simulador D65. Los mejores resultados se obtienen para la combinación configuración multiespectral del sistema y simulador D65 como iluminante.Seguidamente, la precisión de la medida del color y la reconstrucción espectral se analiza en función de los espectros de reflectancia de las muestras de color medidas para determinar si existe algún tipo de correlación entre ambos. Este estudio se lleva a cabo utilizando la configuración multiespectral del sistema y el iluminante D65, la carta CCDC y las muestras Munsell como conjuntos de entrenamiento y prueba. La precisión de la medida del color y la reconstrucción espectral se analizan en función de, por un lado, el área bajo la curva (AUC) de los espectros de reflectancia y, por otro lado, la suavidad de los espectros de reflectancia mediante su Transformada Discreta de Fourier (DFT), que se utiliza frecuentemente en análisis de espectros para determinar la suavidad de las curvas. Respecto al análisis del AUC, la precisión del sistema en la medida del color tiende a mejorar para muestras con AUC de sus espectros de reflectancia mayores, aunque no se puede establecer ninguna relación directa entre ambas. Esta tendencia no se observa en términos de la precisión de la reconstrucción espectral. Una mayor precisión en la reconstrucción espectral se asocia con frecuencia a espectros de reflectancia suaves, aunque tampoco se puede establecer ninguna correlación entre ambos. En cuanto al análisis de la DFT, la precisión en la medida del color parece ser independiente de la forma y/o la suavidad de los espectros de reflectancia, mientras que la mayor precisión en la reconstrucción espectral se asocia con frecuencia a un espectro de reflectancia suave, aunque no se puede establecer una correlación general entre ambas. Una vez completado el minucioso análisis del sistema multiespectral desarrollado y establecidas sus limitaciones en cuanto a precisión en la medida del color y la reconstrucción espectral, la siguiente etapa es determinar si algún otro número y/o combinación de filtros interferenciales disponibles comercialmente permitiría mejorar, al menos teóricamente, la precisión del sistema multiespectral. Con este propósito se lleva a cabo un estudio de simulación de un sistema multiespectral óptimo para la medida del color y la reconstrucción espectral. Este estudio se realiza considerando la respuesta espectral del la cámara CCD 12-bits refrigerada monocromática utilizada y una base de datos de filtros disponibles comercialmente seleccionados entre las bases de datos de Edmund Optics, OptoSigma y CVI. Se observa que la precisión del sistema se mejora al incrementar el número de filtros, aunque esta mejora está limitada y tiende a ser insignificante para un número de filtros superior a 8. Los filtros óptimos tienden a compensar la respuesta espectral de la cámara CCD sobre todo el rango visible pero teniendo en cuenta el inconveniente que suponen el desconocer las transmitancias reales de los filtros (las simulaciones dependen en gran medida de las transmitancias reales de los filtros, que no siempre se pueden simular fácilmente a partir de las especificaciones de los proveedores), la selección de un conjunto de filtros interferenciales con posiciones de pico equidistantes cubriendo todo el rango visible, iguales FWHM que permiten un ligero solapamiento entre ellos, y la mayor transmitancia posible, como se ha hecho en este trabajo, constituye una opción más que aceptable para obtener un sistema multiespectral útil. Finalmente, se comprueba la aplicabilidad del sistema multiespectral desarrollado utilizando, no sólo cartas de color estandarizadas, como son las CCDC, CCCR y las muestras Munsell, sino utilizando también un conjunto de 56 muestras textiles agrupadas en 28 parejas, que fueron especialmente fabricadas para comprobar la aplicabilidad de las fórmulas de diferencia de color, y el simulador D65 como iluminante. Se analizan diferentes combinaciones de conjuntos de entrenamiento y prueba. Los mejores resultados se obtienen, en promedio, utilizando conjuntos de entrenamiento homogéneos en tono y llevando a cabo una clasificación previa de las muestras textiles en tonos. Además, se comprueba la capacidad del sistema multiespectral desarrollado de detectar pequeñas diferencias, tanto en color como en el espectro de reflectancia, entre muestras reales, resultando así ser útil para aplicaciones que requieran discriminación, aunque se obtiene una escasa precisión en la determinación de las diferencias tanto de color como en las reflectancias espectrales entre los pares de muestras textiles considerados. / Nowadays, imaging systems based on CCD cameras are widely used in several fields and, particularly in the field of scientific image, due to its high resolution, high quantum efficiency, wide spectral response, acceptable signal-to-noise ratio, linearity, geometric fidelity, fast response, small size and durability.In spite of this, if a CCD camera is wanted to be used as a measuring instrument, one must bear in mind that CCD cameras are not perfect detectors, but there are various noise sources inherent to their performance that alter the digital levels corresponding to each pixel, distort the real image acquired in an unknown manner, and diminish the radiometric accuracy, the image quality and the resolution.Two of the relatively recent applications of the imaging systems based on CCD cameras are colour measurement and spectral reconstruction. Colour measurement basically consists of estimating the XYZ tristimulus values associated to a colour sample from the system's response digital levels, whereas spectral reconstruction consists of estimating the reflectance spectrum of a colour sample from its corresponding system's response digital levels.Nevertheless, performing colour measurements and/or spectral reconstructions using this kind of devices requires a previous characterization or calibration of the imaging system. On one hand, colour measurement requires to determine the transformation that defines the correspondence between system's digital responses and a colour space independent of the device, such as the XYZ or the CIELAB. This is due to the fact that system's digital responses, even the RGB output signals for a trichromatic imaging system, do not correspond with the device independent tristimulus values based on the CIE standard colorimetric observer. On the other hand, spectral reconstruction requires to determine the transformation that defines the correspondence between system's digital responses and the reflectance spectra space.Methods for colorimetric characterization can be divided in two general categories: methods based on spectral sensitivities, some of which are usually only applied to colorimetric configurations of imaging systems, i.e. with three acquisition channels, due to its growing complexity when the number of acquisition channels is increased, and methods based on a colour sample chart. Methods based on spectral sensitivities require the knowledge of the system's spectral sensitivities for each acquisition channel, which can be previously determined through the spectral characterization of the imaging system.Regarding the methods for spectral reconstruction, their main objective is to reconstruct the reflectance, transmittance or radiance spectra of a colour sample from the corresponding digital responses of the imaging system. These methods are usually applied to multispectral configurations since the linear models of reflectance spectrum used require at least four acquisition channels to be able to estimate real reflectance spectra.In order an imaging system based on a CCD camera can be used as a measuring instrument with high spatial resolution, so that the whole system's detection area is useful for measuring, it is mandatory to correct the spatial non-uniformity of the system's response. Basically two kinds of techniques are used with this purpose. Firstly, the scene-based techniques are based on applying an algorithm to the original or raw image in order to obtain a considerable improvement in image quality at the expense of radiometric accuracy. Secondly, the flat-field correction or spatial non-uniformity correction techniques are based on calibrating the detector by means of two images: a dark image and a uniform field or flat-field image, which are linearly combined with the original or raw image (image to be corrected). These second type techniques allow to use a CCD camera to perform accurate radiometric measurements. Several variants of these flat-field correction or spatial non-uniformity correction techniques can be found in literature. The most general of these variants allow the correction of the spatial non-uniformity of the system's response independently of the spatial non-uniformity of the scene illumination, which is quite useful in several measurement imaging conditions, such as in the case of images corresponding to self-radiating objects.Using an imaging system based on a CCD camera for high spatial resolution colour measurement and/or spectral reconstruction requires applying one of the second type techniques for the spatial non-uniformity correction. In this work, the experimental methodology developed to correct the inherent noise sources of an imaging system based on a CCD camera, and the optimization of a spatial non-uniformity correction algorithm to obtain the best spatial non-uniformity correction possible are presented.The main aim of this work is to develop a multispectral imaging system for colour measurement and spectral reconstruction. The design and development of a prototype of multispectral imaging system in the visible range of the spectrum and its thorough characterization and analysis is presented in this work. For this purpose, an imaging system based on a CCD camera is used. Therefore, in order to be able to perform accurate colour measurements and/or spectral reconstructions with high spatial resolution it will be necessary to carry out, firstly, the noise correction of the system's response, particularly the correction of the spatial non-uniformity, and secondly, the previously mentioned characterization or calibration of the imaging system to be able to obtain the XYZ tristimulus values and/or the reflectance spectra, respectively, from the system's digital responses.Two imaging systems based on a CCD camera are used in this work: an imaging system based on a colour 10-bits CCD camera, and an imaging system based on a monochrome 12-bits cooled CCD camera. Two configuration of this last imaging system are considered: a colorimetric configuration with 3 acquisition channels, and a multispectral configuration with 7 acquisition channels. The spectral characterization is carried out only for the colorimetric configuration of the previously mentioned two imaging systems, in order to be able to apply the method for colorimetric characterization based on the spectral sensitivities of the imaging system.Different methods for colour measurement and spectral reconstruction are applied to the two configurations of the imaging system based on a monochrome 12-bits cooled CCD camera, and compared using all possible combinations of the GretagMacbeth ColorChecker Color Rendition chart (CCCR) and the GretagMacbeth ColorChecker DC chart (CCDC) as training and test sets, in order to determine the most suitable methods for each configuration, i.e., the methods that allow to achieve the best accuracy of both colour measurement and spectral reconstruction for each configuration. At the same time, the performance of the two configurations is also compared in terms of both accuracy of colour measurement and accuracy of spectral reconstruction.The fact that the spectral sensitivities of most of the commercial colour CCD cameras (3 acquisition channels) do not verify the Luther condition, i.e., are not linear transformations of the CIE colour matching functions, seriously limitates the colorimetric applications of the imaging systems based on colour CCD cameras, giving rise to estimated tristimulus values dependent of the illuminant. This property of the spectral sensitivities motivates the use of multispectral imaging systems, since the only way to assure a colour matching for all observers and under changes in illumination is achieving a spectral matching. The most direct method to obtain spectral information of the measured samples is to increase the sampling over the three traditional acquisition channels by means of narrowband filters, which is known as a multispectral imaging system. The application fields of the multispectral imaging systems have increased enormously in last years, fundamentally due to the possibility that offer of estimating accurately the reflectance spectrum at each pixel and, from it, the XYZ tristimulus values avoiding metamerism.The multispectral imaging system designed and developed in this work comprises a monochrome 12-bits cooled CCD camera, a motorized filter wheel controlled via software with a set of narrowband filters, and an objective lens of variable focal length. A set of seven narrowband interference filters covering the whole visible range of the spectrum, with equal FWHM and equidistant central wavelengths, are used following the results obtained in previous works in the NIR region of the spectrum, and extrapolating them to the visible range. Each filter constitutes an acquisition channel of the multispectral imaging system, which corresponds to the multispectral configuration of the imaging system mentioned previously.The first stage before an imaging system based on a CCD camera can be used as a measuring instrument with high spatial resolution is to carry out the correction of the different noise sources inherent to the CCD's performance and, especially, the correction of the spatial non-uniformity of the sensor's response. With this aim, the experimental methodology to correct these noise sources has been developed and a linear algorithm for the spatial non-uniformity correction of the system's response has been optimized.Several analyses have also been carried out throughout this work in order to improve the accuracy of the colour measurement and the spectral reconstruction performed using imaging systems based on CCD cameras.Firstly, considering the basic concepts applied in high dynamic range imaging (HDRI) to obtain a device independent representation of the visual content of a real scene, a luminance adaptation model is proposed to increase the dynamic range of the imaging system by taking images at different exposure times in order to obtain useful digital levels for all the pixels. The application of this luminance adaptation model allows to measure colour at each pixel of the image, increasing the dynamic range of the imaging system by this way.Secondly, the influence of the number of samples of the training set on the accuracy of colour measurement and spectral reconstruction is analyzed in order to determine if there exists a relationship between the accuracy of colour measurement and spectral reconstruction, and the size of the training set. Accuracy of system's performance improves by increasing the size of the training set up to 110 colour samples approximately, and becomes independent of the training set used for training sets having a number of colour samples greater or equal to 110.Next, colour measurement and spectral reconstruction performed using both the colorimetric and the multispectral configurations of the imaging system are analyzed depending on the colour ranges measured, i.e. sets of colour samples grouped by their hue property, with the aim of determining if these configurations are especially sensitive to some hues and/or some other colour properties. Firstly, general tendencies are analyzed using the CCDC chart as training and test sets, and secondly, the 1269 colour patches of the Munsell Book of Color - Matte Collection, classified in 10 Munsell hues and each one of these hues in 4 sub-hues, are used to analyze the influence of homogeneity in hue of the training set on system's performance.Homogeneity in hue of the training set is proved to allow improving meaningfully accuracy of system's performance in terms of both colour measurement and spectral reconstruction. On the other hand, three combinations of training and test sets of Munsell's colour patches are used in order to vary the degree of homogeneity in hue of the training set. Best results are obtained using the most homogeneous in hue training sets.Furthermore, results obtained are also analyzed depending on the colour characteristics of samples measured such as the CIELAB coordinates, and the Munsell hue, value and chroma coordinates. No correlation is observed between accuracy of system's performance and the CIELAB coordinates, whereas accuracy of system's performance tends to get worse for samples having Munsell Values V > 7 - 8.The influence of the illuminant used is also analyzed by comparing results obtained using two illuminants: an incandescent lamp illuminant, which is the one used so far, and a D65 simulator illuminant.Then, accuracy of colour measurement and spectral reconstruction is analyzed depending on the reflectance spectra of the colour samples measured, in order to determine if there exists any kind of correlation between them. This study is performed using the best proved combination of system's configuration and illuminant, which is multispectral configuration and D65 simulator illuminant, and the CCDC chart and the Munsell's colour patches as training and test sets. Accuracy of colour measurement and spectral reconstruction is analyzed depending on, on one hand, the Area Under the Curve (AUC) of reflectance spectra and, on the other hand, on the smoothness of the reflectance spectra by means of their Discrete Fourier Transform (DFT), which is usually used in spectrum analysis to determine the smoothness of curves. Considering the AUC analysis, accuracy of colour measurement tends to improve for the colour samples with higher AUCs of their reflectance spectra, whereas this tendency is not observed for the accuracy of spectral reconstruction. However, any direct relationship cannot be established either between the accuracy of colour measurement and the AUC of the reflectance spectra of colour samples. Considering the DFT analysis, accuracy of colour measurement seems to be independent of the shape and/or the smoothness of the reflectance spectra, whereas the best accuracy of spectral reconstruction is frequently associated to a smooth reflectance spectrum, although any general correlation cannot be established between them.Once thoroughly analyzed the multispectral imaging system developed, and established its limitations in terms of accuracy of colour measurement and spectral reconstruction, next stage is determining if any other number and/or combination of commercially available interference filters would allow to improve, at least theoretically, the accuracy of the multispectral imaging system in terms of colour measurement and spectral reconstruction. For this purpose, a simulation study of an optimum multispectral imaging system for colour measurement and spectral reconstruction is presented. This study is performed considering the spectral response of the monochrome 12-bits cooled CCD camera used and a database of commercially available interference filters selected among the databases of Edmund Optics, OptoSigma and CVI. Accuracy of system's performance is improved in terms of accuracy of both colour measurement and spectral reconstruction with an increasing number of interference filters. Nevertheless, this improvement is limited and tends to be insignificant for more than 8 filters. Optimum filters tend to make up for the spectral response of the CCD camera over the whole visible range but considering the drawback the unknown real spectral transmittances of filters supposes (simulations depend greatly on the real spectral transmittances of filters, which not always can be easily simulated from the specifications provided by suppliers), the selection of a set of interference filters having equidistant peak positions covering the whole visible range, equal FWHMs that allow a slight overlapping between them, and the higher transmittance possible, as it was done in this work, constitutes an acceptable option to obtain a worthy multispectral imaging system.Finally, the applicability of the multispectral imaging system developed is tested not only using standardized colour charts, such as the CCCR, CCDC, and the Munsell's colour patches used so far, but also using real samples, such as a set of 56 textile samples grouped in 28 pairs, which were made specifically to test the applicability of colour difference formulas, and the D65 simulator illuminant. Different combinations of training and test sets are analyzed. Best results are obtained, in average, using training sets homogeneous in hue and carrying out a previous hue classification of the textile samples used as test set. Moreover, the multispectral imaging system developed is proved to be able to detect slight differences both in colour and in reflectance spectra between real samples, making it useful for applications that require discrimination, although a quite low accuracy of system's performance is obtained in detecting both the colour differences and the spectral differences between pairs of textile samples.