Abstract:
Low-level image processing has struggled with image restoration (IR). For visually pleasing results, image priors are crucial. This paper develops a multi-channel and multi-model-based denoising autoencoder network as image prior for IR problem.
First, the RGB-channel image-trained network constructs a prior, which is then used in single-channel grayscale IR tasks. We integrate higher-dimensional network-driven prior information into the iterative restoration procedure using the auxiliary variable technique.
According to weighted aggregation, a multi-model strategy improves network stability and avoids local optima. The proposed algorithm deblurs and deblocks grayscale images efficiently and reliably.
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