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Denoising and demosaicking of color images

Mina Rafinazari
University of Ottawa

January 26, 2017 at  1:30 PM
McConnell Engineering Room 437

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 does not attenuate the spectrum of light. Different four-channel CFAs have been discussed in this research: RGBW-Kodak, RGBW-Bayer and RGBW- 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 different CFA patterns will be discussed for four channel CFAs.

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 pixels 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.