Demosaicing and Superresolution for Color Filter Array via Residual Image Reconstruction and Sparse Representation

A framework of demosaicing and superresolution for color filter array (CFA) via residual image reconstruction and sparse representation is presented.Given the intermediate image produced by certain demosaicing and interpolation technique, a residual image between the final reconstruction image and the intermediate image is reconstructed using sparse representation.The final reconstruction image has richer edges and details than that of the intermediate image. Specifically, a generic dictionary is learned from a large set of composite training data composed of intermediate data and residual data. The learned dictionary implies a mapping between the two data. A specific dictionary adaptive to the input CFA is learned thereafter. Using the adaptive dictionary, the sparse coefficients of intermediate data are computed and transformed to predict residual image. The residual image is added back into the intermediate image to obtain the final reconstruction image. Experimental results demonstrate the state-of-the-art performance in terms of PSNR and subjective visual perception.