A regularization approach for bayer reconstruction in lossy image coding by inverse demosaicing

Color image coding by inverse demosaicing, which exploits the implicit Bayer structure in color images, has a potential to achieve superior performance compared to conventional color image coding methods. The previous framework of inverse demosaicing was limited to lossless and near-lossless data compression, while this paper explores its adaptation to lossy compression. To cope with distortions due to lossy compression, we propose a regularization approach using side color information for the Bayer recovery problem in the decoder. Thanks to careful design of the regularization, the resulting Bayer recovery problem becomes an unconstrained quadratic programming problem, and thus several efficient solvers can be used. A numerical example demonstrates the efficacy of our approach. It can significantly reduce distortions in the recovery of the Bayer data and keep the total bit rate.

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