Remote sensing images are essential for many earth science applications, but their quality can be degraded due to limitations in sensor technology and complex imaging environments. To address this, various remote sensing image deblurring methods have been developed to restore sharp, high-quality images from degraded observational data. However, most traditional model-based deblurring methods usually require predefined hand-craft prior assumptions, which are difficult to handle in complex applications, and most deep learning-based deblurring methods are designed as a black box, lacking transparency and interpretability. In this work, we propose a novel blind deblurring learning framework based on alternating iterations of shrinkage thresholds, alternately updating blurring kernels and images, with the theoretical foundation of network design. Additionally, we propose a learnable blur kernel proximal mapping module to improve the blur kernel evaluation in the kernel domain. Then, we proposed a deep proximal mapping module in the image domain, which combines a generalized shrinkage threshold operator and a multi-scale prior feature extraction block. This module also introduces an attention mechanism to adaptively adjust the prior importance, thus avoiding the drawbacks of hand-crafted image prior terms. Thus, a novel multi-scale generalized shrinkage threshold network (MGSTNet) is designed to specifically focus on learning deep geometric prior features to enhance image restoration. Experiments demonstrate the superiority of our MGSTNet framework on remote sensing image datasets compared to existing deblurring methods.

翻译：遥感图像对许多地球科学应用至关重要，但由于传感器技术的局限性和复杂的成像环境，其质量可能会下降。为解决这一问题，已开发出多种遥感图像去模糊方法，旨在从退化的观测数据中恢复出清晰、高质量的图像。然而，大多数传统的基于模型的去模糊方法通常需要预定义的手工先验假设，这在复杂应用中难以处理；而大多数基于深度学习的去模糊方法被设计为黑箱模型，缺乏透明性和可解释性。本文提出了一种新颖的基于收缩阈值交替迭代的盲去模糊学习框架，通过交替更新模糊核和图像，并以网络设计的理论基础为支撑。此外，我们提出了一种可学习的模糊核近端映射模块，以改进模糊核在核域中的评估。接着，在图像域中，我们提出了一种深度近端映射模块，该模块结合了广义收缩阈值算子和多尺度先验特征提取块。该模块还引入了注意力机制以自适应调整先验重要性，从而避免了手工图像先验项的缺陷。由此，设计了一种新颖的多尺度广义收缩阈值网络（MGSTNet），专门专注于学习深度几何先验特征以增强图像恢复。实验表明，与现有去模糊方法相比，我们的MGSTNet框架在遥感图像数据集上具有优越性。