论文标题
深度概括的图像修复网络
Deep Generalized Unfolding Networks for Image Restoration
论文作者
论文摘要
深度神经网络（DNN）在图像恢复方面取得了巨大成功。但是，大多数DNN方法被设计为黑匣子，缺乏透明度和解释性。尽管提出了一些方法将传统优化算法与DNN相结合，但它们通常需要预定义的降解过程或手工制作的假设，这使得很难处理复杂和现实的应用程序。在本文中，我们提出了一个深刻的概括性展开网络（DGUNET），以进行图像恢复。具体而言，我们在不丧失解释性的情况下将梯度估计策略整合到近端梯度下降（PGD）算法的梯度下降步骤中，将其推动以处理复杂而真实的图像降解。此外，我们在不同的PGD迭代中设计了跨近端映射的阶段间信息途径，以通过多尺度和空间自适应方式纠正大多数深层展开网络（DUN）的内在信息损失。通过将柔性梯度下降和信息近端映射整合在一起，我们将迭代的PGD算法展开到可训练的DNN中。各种图像恢复任务的广泛实验证明了我们方法在最先进的性能，可解释性和概括性方面的优势。源代码可在https://github.com/mc-e/deep-generalized-unfolding-networks-for-image-restoration中获得。
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.
