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Joint Color Space GMMs for CFA Demosaicking

Publication Type : Journal Article

Publisher : IEEE Signal Processing Letters

Source : IEEE Signal Processing Letters, Volume 26, Issue 2, p.232-236 (2019)

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Keywords : Bayer pattern, blue channels, Channel estimation, Color, Color filter array, color filter arrays, color image denoising, color-KSVD algorithm, Correlation, Covariance matrices, demosaicking, Estimation, Gaussian mixture models, Gaussian models, Gaussian processes, GMM analogue, green channels, Image color analysis, image colour analysis, Image denoising, Image filtering, Image segmentation, inter channel correlations, Interpolation, JCS-GMM demosaicking algorithm, joint color space Gaussian mixture model, joint Color space GMM, mosaicked color image, patch-based algorithm, red channels, state-of-the-art demosaicking algorithm

Campus : Amritapuri

School : School of Engineering

Department : Electronics and Communication

Year : 2019

Abstract : We propose a patch-based algorithm for demosaicking a mosaicked color image produced by color filter arrays commonly used in acquiring color images. The proposed algorithm exploits a joint color space Gaussian mixture model (JCS-GMM) prior for jointly characterizing the patches from red, green, and blue channels of a color image. The inter channel correlations captured by the covariance matrices of Gaussian models are exploited to estimate the pixel values missing in the mosaicked image. The proposed JCS-GMM demosaicking algorithm can be seen as the GMM analogue of the Color-KSVD algorithm, which has produced impressive results in color image denoising and demosaicking. We demonstrate that our proposed algorithm achieves superior performance in the case of Kodak and Laurent Condat's databases, and competitive performance in the case of IMAX database, when compared with state-of-the-art demosaicking algorithms.

Cite this Research Publication : Sandeep P. and Jacob, T., “Joint Color Space GMMs for CFA Demosaicking”, IEEE Signal Processing Letters, vol. 26, no. 2, pp. 232-236, 2019.

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