Structured Deep Unfolding Network for Optical Remote Sensing Image Super-Resolution

Single-image super-resolution technology is critical in remote sensing, effectively improving the resolution of target images, with super-resolution algorithms based on deep learning demonstrating superior performance. However, most neural networks present shortcomings, such as a lack of interpretability and requiring a long training time, limiting them in some application scenarios. Moreover, due to the multidegradation factors, tasks put forward higher requirements for the adaptability of algorithms. Therefore, this work develops a structured deep unfolding network (SDUNet), which is adaptable and requires a lower training time by cascading multiple small network modules. Additionally, the unfolding strategy proposed deals with multiple degradations, fully exploiting prior knowledge. The suggested method is challenged against state-of-the-art neural network methods on one optical remote sensing image (ORSI) dataset and one natural image dataset. The experimental results demonstrate our method's effectiveness in requiring less training time, involving fewer parameters, and achieving a higher reconstruction performance for ORSI super-resolution.