Remote sensing images are essential for many applications of the earth's sciences, but their quality can usually 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 and high-quality images from degraded observational data. However, most traditional model-based deblurring methods usually require predefined {hand-crafted} prior assumptions, which are difficult to handle in complex applications. On the other hand, deep learning-based deblurring methods are often considered as black boxes, lacking transparency and interpretability. In this work, we propose a new blind deblurring learning framework that utilizes alternating iterations of shrinkage thresholds. This framework involves updating blurring kernels and images, with a theoretical foundation in network design. Additionally, we propose a learnable blur kernel proximal mapping module to improve the accuracy of the blur kernel reconstruction. Furthermore, we propose a deep proximal mapping module in the image domain, which combines a generalized shrinkage threshold with a multi-scale prior feature extraction block. This module also incorporates an attention mechanism to learn adaptively the importance of prior information, improving the flexibility and robustness of prior terms, and avoiding limitations similar to hand-crafted image prior terms. Consequently, we design a novel multi-scale generalized shrinkage threshold network (MGSTNet) that focuses specifically on learning deep geometric prior features to enhance image restoration. Experimental results on real and synthetic remote sensing image datasets demonstrate the superiority of our MGSTNet framework compared to existing deblurring methods.

翻译：遥感图像在地球科学的多项应用中至关重要，但受限于传感器技术和复杂成像环境，其质量通常可能退化。针对这一问题，研究者已开发多种遥感图像去模糊方法，旨在从退化的观测数据中恢复清晰的高质量图像。然而，传统基于模型的去模糊方法大多需要预设的人工先验假设，这难以应对复杂应用场景。另一方面，基于深度学习的去模糊方法常被视为黑箱，缺乏透明性与可解释性。本文提出一种利用收缩阈值交替迭代的盲去模糊学习框架，该框架通过交替更新模糊核与图像，具有坚实的网络设计理论基础。同时，我们提出一种可学习的模糊核近端映射模块，以提高模糊核重建精度。此外，在图像域中，我们设计了一种深度近端映射模块，该模块将广义收缩阈值与多尺度先验特征提取块相结合，并引入注意力机制以自适应学习先验信息的重要性，提升先验项的灵活性与鲁棒性，避免传统人工先验项的限制。基于此，我们构建了一种新型多尺度广义收缩阈值网络（MGSTNet），专门用于学习深度几何先验特征以增强图像恢复效果。在真实与合成遥感图像数据集上的实验结果表明，与现有去模糊方法相比，本研究所提出的MGSTNet框架具有显著优越性。