Deep Generalized Unfolding Networks for Image Restoration (2204.13348v1)

Abstract: 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.

• The paper presents DGUNet, which unfolds the Proximal Gradient Descent algorithm into a trainable network to enhance interpretability and restoration quality.
• It introduces the Flexible Gradient Descent Module and Informative Proximal Mapping Module to handle variable degradation and improve feature fusion.
• The model achieves state-of-the-art results in diverse tasks such as denoising, deblurring, and compressive sensing in both synthetic and real-world settings.

Overview of "Deep Generalized Unfolding Networks for Image Restoration"

The paper "Deep Generalized Unfolding Networks for Image Restoration" presents an innovative approach to address the challenges of image restoration by leveraging the strengths of both model-based methods and deep learning algorithms. The authors propose a novel architecture called the Deep Generalized Unfolding Network (DGUNet) that promises not only interpretability but also applicability to complex and real-world image degradation scenarios.

Image restoration (IR) encompasses a range of tasks, including denoising, deblurring, deraining, and compressive sensing. Traditionally, IR methods are either model-based or deep learning-based, each with its limitations. Model-based approaches provide interpretability but lack robustness and efficiency, while deep learning methods offer impressive performance but function as a "black box" with little interpretability.

The proposed DGUNet bridges this gap by unfolding the Proximal Gradient Descent (PGD) algorithm into a trainable deep neural network. This unfolding is achieved through two major components: the Flexible Gradient Descent Module (FGDM) and the Informative Proximal Mapping Module (IPMM). The design allows DGUNet to adaptively address known and unknown degradation matrices, thereby enhancing its applicability to a wide range of IR tasks.

Key Contributions

1. Flexible Gradient Descent Module: The FGDM is engineered to tackle both known and unknown degradation matrices, thus providing flexibility that traditional PGD lacks. By utilizing trainable components for gradient estimation, the approach eliminates the need for handcrafted assumptions about degradation.
2. Informative Proximal Mapping Module: Employing a multi-scale and spatial-adaptive normalization technique, the IPMM addresses the intrinsic information loss in deep unfolding networks (DUN). This feature facilitates efficient feature fusion across different iterations, enhancing overall restoration quality.
3. Inter-stage Information Pathways: This novel design element ensures a robust flow of information across different scales and stages, remedying the information distortion often inherent in DUNs. The spatial-adaptive approach ensures a refined memory of features, empowering the model to yield better reconstructions.
4. End-to-End Trainability with Interpretability: DGUNet maintains interpretability by basing its architecture on PGD iterations, while also benefiting from end-to-end training typical of deep learning models. This approach facilitates the processing of real-world image degradation without relying on predefined degradation descriptions.

Experimental Results and Implications

DGUNet exhibits state-of-the-art performance across diverse IR tasks, demonstrating robustness and superior restoration quality in settings ranging from synthetic to real-world datasets. Notable improvements are observed in challenging scenarios such as real image denoising and compressive sensing with low sampling ratios. The numerical results presented in the paper underscore DGUNet's effectiveness and efficiency when compared to recent leading methods.

The combination of interpretability and high performance suggests important implications for practical applications. DGUNet's ability to generalize across various tasks makes it a promising candidate for deployment in practical imaging systems where understanding and adjusting the restoration process dynamically are crucial.

Future Perspectives

DGUNet signifies an important step towards harmonizing the interpretability of model-based methods with the adaptability of deep learning. Future work might explore extending this framework to other inverse problems in computer vision, as well as further optimizing the network's computational efficiency. Additionally, integration with contemporary AI frameworks, such as MindSpore, is proposed as an avenue for broader deployment and accessibility of the methodology.

In summary, DGUNet represents a significant advancement in image restoration, combining methodological rigor with empirical effectiveness. Its proposed framework opens new directions for both theoretical exploration and practical innovation in the field of computer vision.