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

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 generated SR images. The two networks iterate alternately, forming an end-to-end trainable network. More importantly, the SRConvNext utilizes the degradation kernel estimated by the RFGKCorrection for reconstruction, allowing the SRConvNext to perform well even if the degradation kernel deviates from the real-world scenario. In experimental terms, three levels of noise and five Gaussian blur kernels are considered on the NWPU-RESISC45 remote sensing image dataset for synthesizing degraded remote sensing images to train and test. Compared to existing super-resolution methods, the experimental results demonstrate that our proposed approach achieves significant reconstruction advantages in both quantitative and qualitative evaluations. Additionally, the UCMERCED remote sensing dataset and the real-world remote sensing image dataset provided by the “Tianzhi Cup” Artificial Intelligence Challenge are utilized for further testing. Extensive experiments show that our method delivers more visually plausible results, demonstrating the potential of real-world application.