Demosaicking and zooming for color filter array via residual image reconstruction

A novel strategy of demosaicking and zooming for Color Filter Array via residual image reconstruction is presented. The proposed scheme depends on certain demosaicking and zooming techniques. Given demosaicking and zooming methods, a residual image between 'genuine' image and the initial demosaicked and enlarged image is reconstructed using a dictionary and sparse coding. The 'genuine' image has richer edges and details than that of the initial result image. The dictionary is obtained by learning a training image set and sparse representation. Once the residual image is reconstructed, scaled version of it is added back to the initial result image to get final result. As R-G difference and B-G difference are smoother than individual channels, we search G channel, R-G difference and B-G difference instead of individual three channels. Considering implementation efficiency, only 'genuine' G channel is reconstructed using the proposed method, the demosaicked R-G difference and B-G difference are just zoomed with simple bicubic interpolation. The experiment results have demonstrated the state-of-the-art results in both visual perception and PSNR.