| Summary: | 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.
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