Multistep Tunable Super-Resolution for Remote Sensing Image Clarity Enhancement

Abstract

Recently, with the rapid development of deep learning technology, remote sensing image super-resolution (SR) reconstruction has become an important research direction in the field of computer vision. Traditional deep learning-based SR methods often suffer from oversmoothed reconstruction results and lack of high-frequency details. Subsequent methods have been improved to produce SR results that better align with human visual perception. However, these methods tend to introduce unpleasant artifacts into the reconstructed images. Currently diffusion-based approaches leverage forward noise addition and backward denoising processes to generate artifact-free SR images that also conform to visual perception. Nevertheless diffusion-based models still suffer from unstable SR performance. To address these issues, we propose a multistep tunable SR network named MTSR. Our network simulates the mapping process from low-resolution inputs to high-resolution outputs and introduces adjustable parameters. This design enables our method to produce nonsmooth SR results without artifacts while preserving rich and realistic textural details. Meanwhile, this study also optimizes the training network, enabling it to better capture the intermediate information transformations from low-resolution to high-resolution images. We conduct experiments on three datasets, and the experimental results demonstrate that our method achieves excellent texture recovery performance.