Multi-Degradation Super-Resolution Reconstruction for Remote Sensing Images with Reconstruction Features-Guided Kernel Correction

作者：Yi Qin, Haitao Nie, Jiarong Wang, Huiying Liu, Jiaqi Sun, Ming Zhu, Jie Lu, Qi Pan · 发表于：Remote Sensing · 年份：2024 · DOI：10.3390/rs16162915 · 被引用次数：8 · 研究领域：Advanced Image Processing Techniques、Advanced Image Fusion Techniques、Image and Signal Denoising Methods

A variety of factors cause a reduction in remote sensing image resolution. Unlike super-resolution (SR) reconstruction methods with single degradation assumption, multi-degradation SR methods aim to learn the degradation kernel from low-resolution (LR) images and reconstruct high-resolution (HR) images more suitable for restoring the resolution of remote sensing images. However, existing multi-degradation SR methods only utilize the given LR images to learn the representation of the degradation kernel. The mismatches between the estimated degradation kernel and the real-world degradation kernel lead to a significant deterioration in performance of these methods. To address this issue, we design a reconstruction features-guided kernel correction SR network (RFKCNext) for multi-degradation SR reconstruction of remote sensing images. Specifically, the proposed network not only utilizes LR images to extract degradation kernel information but also employs features from SR images to correct the estimated degradation kernel, thereby enhancing the accuracy. RFKCNext utilizes the ConvNext Block (CNB) for global feature modeling. It employs CNB as fundamental units to construct the SR reconstruction subnetwork module (SRConvNext) and the reconstruction features-guided kernel correction network (RFGKCorrector). The SRConvNext reconstructs SR images based on the estimated degradation kernel. The RFGKCorrector corrects the estimated degradation kernel by reconstruction features from the g...