Most digital cameras capture images through Color Filter Arrays (CFA), and reconstruct the full color image from the CFA image. Each CFA pixel only captures one primary color component at each pixel location; the other primary components will be estimated using information from neighboring pixels. During the demosaicking algorithm, the unknown color components will be estimated at each pixel location. Most of the demosaicking algorithms use the RGB Bayer CFA pattern with Red, Green and Blue filters. Some other CFAs contain four color filters. The additional filter is a panchromatic/white filter, and it usually receives the full light spectrum. In this research, we studied and compared different four channel CFAs with panchromatic/white filter, and compared them with three channel CFAs. An appropriate demosaicking algorithm has been developed for each CFA. The most well-known three-channel CFA is Bayer. The Fujifilm X-Trans pattern has been studied in this work as another three-channel CFA with a different structure. Three different four-channel CFAs have been discussed in this research: RGBW-Kodak, RGBW-Bayer and RGBW- $5 \times 5$. The structure and the number of filters for each color are different for these CFAs. Since the Least-Square Luma-Chroma Demultiplexing method is a state of the art demosaicking method for the Bayer CFA, we designed the Least-Square method for RGBW CFAs. The effect of noise on different CFA patterns will be discussed for four channel CFAs. The Kodak database has been used to evaluate our non-adaptive and adaptive demosaicking methods as well as the optimized algorithms with the least square method. The captured values of white (panchromatic/clear) filters in RGBW CFAs have been estimated using red, green and blue filter values. Sets of optimized coefficients have been proposed to estimate the white filter values accurately. The results have been validated using the actual white values of a hyperspectral image dataset. A new denoising-demosaicking method for RGBW-Bayer CFA has been presented in this research. The algorithm has been tested on the Kodak dataset using the estimated value of white filters and a hyperspectral image dataset using the actual value of white filters, and the results have been compared. The results in both cases have been compared with the previous works on RGB-Bayer CFA, and it shows that the proposed algorithm using RGBW-Bayer CFA is working better than RGB-Bayer CFA in presence of noise.

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To reduce cost, many digital cameras use a single sensor array instead of using three arrays for the red, green and blue. Thus at each pixel location only the red, green or blue intensity value is available. And to generate a complete color image, the camera must estimate the missing two values at each pixel location .Color filter arrays are used to capture only one portion of the spectrum (Red, Green or Blue) at each location. Various arrangements of the Color Filter Array (CFA) are possible, but the Bayer array is the most commonly used arrangement and we will deal exclusively with the Bayer array in this thesis. Since each of the three colors channels are effectively downsampled, it leads to aliasing artifacts. This thesis will analyze the effects of aliasing in the frequency- domain and present a method to reduce the deterioration in image quality due to aliasing artifacts. Two reference algorithms, AH-POCS (Adams and Hamilton - Projection Onto Convex Sets) and Adaptive Homogeneity-Directed interpolation, are discussed in de- tail. Both algorithms use the assumption that there is high correlation in the high- frequency regions to reduce aliasing. AH-POCS uses alias cancellation technique to reduce aliasing in the red and blue images, while the Adaptive Homogeneity-Directed interpolation algorithm is an edge-directed algorithm. We present here an algorithm that combines these two techniques and provides a better result on average when compared to the reference algorithms.

In this thesis, we discuss three image processing topics: High Dynamic Range (HDR) image creation in scenes with motion, Tone Mapping (TM), and Demosaicking. The first part of this thesis focuses on the creation of HDR images using gradient fusion techniques, and proposes a method that deals with motion and avoids bleeding and ghost artifacts. In the second part, we tackle the TM problem, whose goal is to produce a low dynamic range picture from an HDR image that reproduces the sensation of an observer in the scene. We review the perceptual principles that we find important for TM purposes and present a new method that compares well to the state of the art. Finally, we propose a new method to reconstruct the three color channels of a picture taken with a Bayer filter. This problem is called Demosaicking and will be presented in the third part of this thesis. / En esta tesis tratamos tres temas de procesamiento de imagen: creación de imágenes de alto rango dinámico o HDR, Tone Mapping (TM) y Demosaicking. En la primera parte proponemos un método para la creación de imágenes HDR con movimiento que permite generar resultados sin artefactos de tipo bleeding y ghosting. En la segunda parte de la tesis tratamos el problema de TM cuyo objetivo es comprimir el rango dinámico de una imagen HDR para ser mostrada en una pantalla o impresa, simulando lo mejor posible la percepción de un sujeto en la escena. Presentaremos los principios sicofísicos que consideramos relevantes para TM y propondremos un método nuevo que mejora los resultados del estado del arte. Finalmente, en la tercera parte presentamos un método de Demosaicking o reconstrucción de los tres canales de color de una imagen tomada con un filtro de Bayer.