Multi-Noise and Multi-Channel Derived Prior Information for Grayscale Image Restoration

Image restoration is an extensively studied area with lots of outstanding algorithms developed. Nevertheless, most existing methods still have some limitations that only apply to a single tailored restoration task or suffer from long iterative reconstruction time or yield unstable results. To address these challenges, this work presents a multi-noise and multi-channel enhanced Deep Mean-Shift Prior (MEDMSP) for grayscale IR tasks. Specifically, we draw valuable high-dimensional prior knowledge by learning a multi-noise stimulated DMSP network from color images with RGB-channels. Variable augmentation technique is then adopted for incorporating the higher-dimensional network prior into the iterative reconstruction procedure. MEDMSP has been evaluated on different IR tasks and compared to a variety of state-of-the-art methods. Experimental results show that the proposed method has better capability in image deblurring and accurate compressive sensing reconstructions in terms of both visual and quantitative comparisons.