Learning Deep Gradient Descent Optimization for Image Deconvolution

IEEE transactions on neural networks and learning systems, pp. 1-15, 2020.

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Abstract:

As an integral component of blind image deblurring, non-blind deconvolution removes image blur with a given blur kernel, which is essential but difficult due to the ill-posed nature of the inverse problem. The predominant approach is based on optimization subject to regularization functions that are either manually designed or learned fro...More

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