CSwT-SR: Conv-Swin Transformer for Blind Remote Sensing Image Super-Resolution With Amplitude-Phase Learning and Structural Detail Alternating Learning

Image super-resolution (SR) stands as a pivotal process in the domains of image processing and computer vision, finding diverse applications in film, television, photography, surveillance, medical imaging, and remote sensing. In the context of remote sensing images (RSIs), the inherent challenge arises from low spatial resolution caused by factors such as sensor noise, orbit height, and weather conditions, necessitating SR reconstruction. An evident limitation of prevailing methods lies in their dependence on idealized fixed degradation models, which fail to capture the intricate degradation processes unique to remote sensing scenes. In response to these constraints, this article introduces an innovative blind image super-resolution reconstruction method tailored for remote sensing images. The proposed approach integrates convolution with a transformer and incorporates an amplitude-phase learning module (ALM) to comprehensively capture local and long-range dependencies while enhancing frequency information. The iterative optimization strategy refines texture information by carefully balancing structural and detail elements. Key contributions include a holistic approach to remote sensing image SR, ALM integration for precise feature representation, and the introduction of a patch-based frequency loss mechanism for evaluating frequency-domain features. Rigorous experiments demonstrate that compared with other state-of-the-art (SOTA) methods, the proposed algorithm delivers SR results with exceptional visual perception quality across three distinct remote sensing datasets.