  Deep neural networks (DNN) have achieved great success in image restoration.
However, most DNN methods are designed as a black box, lacking transparency and
interpretability. Although some methods are proposed to combine traditional
optimization algorithms with DNN, they usually demand pre-defined degradation
processes or handcrafted 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 gradient
descent step of the Proximal Gradient Descent (PGD) algorithm, driving it to
deal with complex and real-world image degradation. In addition, we design
inter-stage information pathways across proximal mapping in different PGD
iterations to rectify the intrinsic information loss in most deep unfolding
networks (DUN) through a multi-scale and spatial-adaptive way. By integrating
the flexible gradient descent and informative proximal mapping, we unfold the
iterative PGD algorithm into a trainable DNN. Extensive experiments on various
image restoration tasks demonstrate the superiority of our method in terms of
state-of-the-art performance, interpretability, and generalizability. The
source code is available at
https://github.com/MC-E/Deep-Generalized-Unfolding-Networks-for-Image-Restoration.
