Deep Generalized Unfolding Networks for Image Restoration

Deep neural networks (DNN) have achieved great suc-cess in image restoration. However, most DNN methods are designed as a black box, lacking transparency and inter-pretability. Although some methods are proposed to combine traditional optimization algorithms with DNN, they usually demand pre-defined degradation processes or hand-crafted assumptions, making it difficult to deal with complex and real-world applications. In this paper, we propose a Deep Generalized Unfolding Network (DGUNet) for image restoration. Concretely, without loss of interpretability, we integrate a gradient estimation strategy into the gradi-ent descent step of the Proximal Gradient Descent (PGD) algorithm, driving it to deal with…

• NN
  National Natural Science Foundation of China

  Award: 61902009
• SF
  Shenzhen Fundamental Research Program

  Award: GXWD20201231165807007-20200807164903001