Skip to Main Content
Typical consumer digital cameras sense only one out of three color components per image pixel. The problem of demosaicing deals with interpolating those missing color components. In this paper, we present compressive demosaicing (CD), a framework for demosaicing natural images based on the theory of compressed sensing (CS). Given sensed samples of an image, CD employs a CS solver to find the sparse representation of that image under a fixed sparsifying dictionary Ψ. As opposed to state of the art CS-based demosaicing approaches, we consider a clear distinction between the interchannel (color) and interpixel correlations of natural images. Utilizing some well-known facts about the human visual system, those two types of correlations are utilized in a nonseparable format to construct the sparsifying transform Ψ. Our simulation results verify that CD performs better (both visually and in terms of PSNR) than leading demosaicing approaches when applied to the majority of standard test images.