Learning Discriminative Shrinkage Deep Networks for Image Deconvolution. (arXiv:2111.13876v2 [cs.CV] UPDATED)

Most existing methods usually formulate the non-blind deconvolution problem
into a maximum-a-posteriori framework and address it by manually designing
kinds of regularization terms and data terms of the latent clear images.
However, explicitly designing these two terms is quite challenging and usually
leads to complex optimization problems which are difficult to solve. In this
paper, we propose an effective non-blind deconvolution approach by learning
discriminative shrinkage functions to implicitly model these terms. In contrast
to most existing methods that use …

arxiv cv image learning networks shrinkage