MAIN: Multibranch Attention Integration Network for Degraded Remote-Sensing Image Super-Resolution.

Remote-sensing images (RSIs) are limited by low-resolution (LR) imaging and a variety of degradation effects, posing significant challenges for high-level visual tasks. Traditional deep-learning-based super-resolution (SR) algorithms struggle to optimize these issues end-to-end, which inhibits the algorithm's lightweight design and restricts its application in real-time scenarios. To address this, we introduce a multibranch attention integration network (MAIN) for the degraded RSI SR. This network features two key functional components: a multiscale feature perception (MsFP) module and a multibranch attention integration block (MAIB). The MsFP module, a lightweight convolution-based architecture, is designed primarily to counteract degradation effects and depict low-dimensional features. MAIB, a multibranch parallel layout, employs attention mechanisms to enable high-level semantic information extraction and context-aware learning. Furthermore, we utilize modulation transfer functions (MTFs) to emulate various degradation effects, thus creating a dedicated dataset for degraded RSI SR, called DeRSSA. Extensive experimental evidence shows that MAIN exceeds the performance of the existing state-of-the-art method for the degraded RSI SR task.