Luminance driven sparse representation based demosaicking

Typical consumer cameras sense at each pixel only one out of the three color components the representation of a color image requires. Then the missing components are estimated via a procedure referred to as demosaicking. The recent spread of sparse regularization approaches for signal reconstruction purposes has extended to demosaicking algorithms too. In this paper, starting from a sparse representation based demosaicking algorithm recently appeared in the literature, a new one is devised. The proposed algorithm effectively estimates the original luminance component from the acquired data and then uses it to guide the sparsity based reconstruction of the full-resolution image. This hybrid approach to demosaicking allows the new algorithm to outperform the past one and to compete with leading demosaicking algorithms, both in terms of PSNR measure and visual quality.

Presented at:
2014 IEEE International Conference on Image Processing (ICIP), Paris, France, October 2014

 Record created 2017-10-03, last modified 2018-09-13

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