Multispectral demosaicing via non-local low-rank regularization

Demosaicing is an essential technique in filter array (FA) based color and multispectral imaging. It aimes to recover missing pixels at different spectrum bands. The existing methods are limited to specific FAs and local regularization. To enhance generalization on different FA structures and improve reconstruction quality, here we present a non-local low-rank regularized demosaicing method, based on the non-local grouped sparsity of natural images. Specifically, the optimization model consists of two parts, including the regularization term of image formation model, and the low-rank term of non-local grouped image patches. The two terms ensure to remove noise and distortion while preserving image details. The model is solved by the weighted nuclear norm minimization and the alternating direction multiplier method framework. Experiments validate that the proposed algorithm has good generalization performance on both different FA patterns and channel numbers. The reconstruction accuracy is improved compared with the existing demosaicing algorithms.