Abstract This thesis describes research into the field of image restoration. Restoration is a process by which an image suffering some form of distortion or degradation can be recovered to its original form. Two primary concepts within this field have been investigated. The first concept is the use of a Hopfield neural network to implement the constrained least square error method of image restoration. In this thesis, the author reviews previous neural network restoration algorithms in the literature and builds on these algorithms to develop a new faster version of the Hopfield neural network algorithm for image restoration. The versatility of the neural network approach is then extended by the author to deal with the cases of spatially variant distortion and adaptive regularisation. It is found that using the Hopfield-based neural network approach, an image suffering spatially variant degradation can be accurately restored without a substantial penalty in restoration time. In addition, the adaptive regularisation restoration technique presented in this thesis is shown to produce superior results when compared to non-adaptive techniques and is particularly effective when applied to the difficult, yet important, problem of semi-blind deconvolution. The second concept investigated in this thesis, is the difficult problem of incorporating concepts involved in human visual perception into image restoration techniques. In this thesis, the author develops a novel image error measure which compares two images based on the differences between local regional statistics rather than pixel level differences. This measure more closely corresponds to the way humans perceive the differences between two images. Two restoration algorithms are developed by the author based on versions of the novel image error measure. It is shown that the algorithms which utilise this error measure have improved performance and produce visually more pleasing images in the cases of colour and grayscale images under high noise conditions. Most importantly, the perception based algorithms are shown to be extremely tolerant of faults in the restoration algorithm and hence are very robust. A number of experiments have been performed to demonstrate the performance of the various algorithms presented.

This paper presents a set of Image Registration, Image Integration, interpolation and image restoration and other technology, the number of low-resolution images synthesized high-resolution image. Relative to the existing image fusion technology, the method provided in this paper has more advantages, such as: (1) high-precision value; (2)low computation cost; (3)a compact system; (4) applicable to noise images; (5) robotic and automatic performance.

Progress in multimedia technologies has resulted in a plethora of services and devices that capture, compress, transmit and display audiovisual stimuli. Humans -- the ultimate receivers of such stimuli -- now have access to visual entertainment at their homes, their workplaces as well as on mobile devices. With increasing visual signals being received by human observers, in the face of degradations that occur to due the capture, compression and transmission processes, an important aspect of the quality of experience of such stimuli is the \emph{perceived visual quality}. This dissertation focuses on algorithm development for assessing such visual quality of natural images, without need for the `pristine' reference image, i.e., we develop computational models for no-reference image quality assessment (NR IQA). Our NR IQA model stems from the theory that natural images have certain statistical properties that are violated in the presence of degradations, and quantifying such deviations from \emph{naturalness} leads to a blind estimate of quality. The proposed modular and easily extensible framework is distortion-agnostic, in that it does not need to have knowledge of the distortion afflicting the image (contrary to most present-day NR IQA algorithms) and is not only capable of quality assessment with high correlation with human perception, but also is capable of identifying the distortion afflicting the image. This additional distortion-identification, coupled with blind quality assessment leads to a framework that allows for blind general-purpose image repair, which is the second major contribution of this dissertation. The blind general-purpose image repair framework, and its exemplar algorithm described here stem from a revolutionary perspective on image repair, where the framework does not simply attempt to ameliorate the distortion in the image, but to ameliorate the distortion, so that visual quality at the output is maximized. Lastly, this dissertation describes a large-scale human subjective study that was conducted at UT to assess human behavior and opinion on visual quality of videos when viewed on mobile devices. The study lead to a database of 200 distorted videos, which incorporates previously studied distortions such as compression and wireless packet-loss, and also dynamically varying distortions that change as a function of time, such as frame-freezes and temporally varying compression rates. This study -- the first of its kind -- involved over 50 human subjects and resulted in 5,300 summary subjective scores and time-sampled subjective traces of quality for multiple displays. The last part of this dissertation analyzes human behavior and opinion on time-varying video quality, opening up an extremely interesting and relevant field for future research in the area of quality assessment and human behavior. / text

There has been an increase in demand for the wafer-level test techniques that evaluates the functionality and performance of the wafer chips before packaging them, since the trend of integrated circuits are getting more sophisticated and smaller in size. Throughout the wafer-level test, the semiconductor manufacturers are able to avoid the unnecessary packing cost and to provide early feedback on the overall status of the chip fabrication process. A probe card is a module of wafer-level tester, and can detect the defects of the chip by evaluating the electric characteristics of the integrated circuits(IC's). A probe card analyzer is popularly utilized to detect such a potential probe card failure which leads to increase in the unnecessary manufacture expense in the packing process. In this paper, a new probe card analysis strategy has been proposed. The main idea in conducting probe card analysis is to operate the vision-based inspection on-the- y while the camera is continuously moving. In doing so, the position measurement from the encoder is rstly synchronized with the image data that is captured by a controlled trigger signal under the real-time setting. Because capturing images from a moving camera creates blurring in the image, a simple deblurring technique has been employed to restore the original still images from blurred ones. The main ideas are demonstrated using an experimental test bed and a commercial probe card. The experimental test bed has been designed that comprises a micro machine vision system and a real-time controller, the con guration of the low cost experimental test bed is proposed. Compared to the existing stop-and-go approach, the proposed technique can substantially enhance the inspection speed without additional cost for major hardware change.

This dissertation proposes a comprehensive working flow for image retrieval. It contains four components: denoising, restoration, color features extraction, and depth feature extraction. We propose a visual perceptual descriptor (VPD) to extract color features from an image. Gradient direction is calculated at each pixel, and the VPD is moved over the entire image to locate regions with similar gradient direction. Color features are extracted only at these pixels. Experiments demonstrate that VPD is an effective and reliable descriptor in image retrieval. We propose a novel depth feature for image retrieval. Regarding any 2D image as the convolution of a corresponding sharp image and a Gaussian kernel with unknown blur amount. Sparse depth map is computed as the absolute difference of the original image and its sharp version. Depth feature is extracted as the nuclear norm of the sparse depth map. Experiments validate the effectiveness of this approach on depth recovery and image retrieval. We present a model for image denoising. A gradient item is incorporated, and can be merged into the original model based on geometric measure theory. Experiments illustrate this model is effective for image denoising, and it can improve the retrieval performance by denoising a query image. A model is proposed for image restoration. It is an extension of the traditional singular value thresholding (SVT) algorithm, addressing the issue that SVT cannot recover a matrix with missing rows or columns. Proposed is a way to fill such rows and columns, and then apply SVT to restore the damaged image. The pre-filled entries are recomputed by averaging its neighboring pixels. Experiments demonstrate the effectiveness of this model on image restoration, and it can improve the retrieval performance by restoring a damaged query image. Finally, the capability of this working flow is tested. Experiments demonstrate its effectiveness in image retrieval.

The purpose of this thesis is to develop a mathematical approach and associated software implementation for deconvolution of two-dimensional Transmission Electron Microscope (TEM) images. The focus is on TEM images of weakly scattering amorphous biological specimens that mainly produce phase contrast. The deconvolution is to remove the distortions introduced by the TEM detector that are modeled by the Modulation Transfer Function (MTF). The report tests deconvolution of the TEM detector MTF by Wiener _ltering and Tikhonov regularization on a range of simulated TEM images with varying degree of noise.The performance of the two deconvolution methods are quanti_ed by means of Figure of Merits (FOMs) and comparison in-between methods is based on statistical analysis of the FOMs.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第21208号 / 情博第661号 / 新制||情||114(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 美濃 導彦, 教授 河原 達也, 教授 中村 裕一 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Lawrence Livermore National Laboratory's (LLNL) National Ignition Facility (NIF) uses a variety of diagnostics and image capturing optics for collecting data in High Energy Density Physics (HEDP) experiments. However, every image capturing system causes blurring and degradation of the images captured. This degradation can be mathematically described through a camera system's Point Spread Function (PSF), and can be reversed if the system's PSF is known. This is deconvolution, also called image restoration. Many PSFs can be determined experimentally by imaging a point source, which is a light emitting object that appears infinitesimally small to the camera. However, NIF's Kirkpatrick-Baez Optic (KBO) is more difficult to characterize because it has a spatially-varying PSF. Spatially varying PSFs make deconvolution much more difficult because instead of being 2-dimensional, a spatially varying PSF is 4-dimensional. This work discusses a method used for modeling the KBO's PSF by modeling it as the sum of products of two basis functions. This model assumes separability of the four dimensions of the PSF into two, 2-dimensional basis functions. While previous work would assume parametric forms for some of the basis functions, this work attempts to only use numeric representations of the basis functions. Previous work also ignores the possibility of non-linear magnification along each image axis, whereas this work successfully characterizes the KBO's non-linear magnification. Implementation of this model gives exceptional results, with the correlation coefficient between a model generated image and an experimental image as high as 0.9994. Modeling the PSF with high accuracy lays the groundwork to allow for deconvolution of images generated by the KBO.

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Syftet med denna studie var att ur ett public relations-perspektiv studera polisens kriskommunikationsretorik i samband med kritik från allmänheten. Denna uppsats har utgått från två fall; fallet i arresten och kvinnoregistret. I uppsatsen har en kvalitativ textanalys använts på sammanlagt 14 texter från polisens egna kanaler. Den teori som använts för att urskilja vilka försvarsstrategier som polisen använt i materialet är image restoration theory. Dessutom har ett public relations-perspektiv använts för att förstå varför försvarsstrategierna använts samt för att belysa ett annat relevant public relationsbegrepp, nämligen öppenhet. Resultatet av analysen blev bland annat att polisen i båda fallen var konsekventa med sin information och att de använt en strategi mer än andra i respektive fall. En slutsats som dras är att allmänhetens förtroende är mycket viktigt för polisen. En annan slutsats som dras är att öppenhet anses vara viktigt, inte bara av tidigare forskning, utan också av polisen själv. / The purpose of this study was by using a public relations perspective study the Swedish police’s crisis communication when criticism from the multitude has occurred. This thesis point of departure has been two cases; fallet i arresten and kvinnoregistret. A qualitative textual analysis has been made on 14 articles from the police homepage and Facebook. The theory that has been used to distinguish what image repair strategies that the police has used is image restoration theory. A public relations perspective has also been applied to produce an understanding for why the image repair strategies has been used, as well as to provide the thesis with another concept important to public relations; openness. The result of the analysis has been among other things that the police in both of the cases were consistent with their information and that they primarily has used one of the image repair strategies. A conclusion is that the multitudes confidence in the police is very important for the police. Another conclusion is that openness is considered important, not only to previous researchers but by the police itself.