Single Image Super-Resolution using Real World Remote Sensing Imagery

Abstract

In the realm of remote sensing (RS), the demand for improved spatial resolution has per sisted across multiple domains despite the exponential rise in satellite images. Single Im age Super-Resolution (SISR) aims to bridge this gap, and many state-of-the-art (SOTA) deep learning models have been utilized for this purpose. However, the generation of Low Resolution (LR) image pairs from High Resolution (HR) images is questionable, as most datasets are synthetically generated either by bicubic down-sampling or by mod eling degradation with blur kernels and imaging noises. Therefore, SISR models trained on such datasets cannot perfectly model real-world scenarios. This study proposes a novel Real World Super-Resolution GAN (RWSRGAN) which is powered by Attention Enhanced Residual in Residual Network (ARRDNet) along with a weighted combi nation loss function. It also presents a comparative analysis of different CNN-based, GAN-based, Transformer-based SR models, and Image-to-Image Translation models for the SISR task. These methods are trained on the WorldStrat dataset, a real-world RS dataset comprising LR and HR pairs from two different satellites, Sentinel-2 and SPOT 6/7, respectively, with a resolution ratio of approximately 6. Our experiments show that the proposed RWSRGAN yield better qualitative and quantitative results as compared to the existing SOTA models, and outperform most of them on a variety of distortion-based and perception-based metrics.