Lightweight Super-Resolution Reconstruction Vision Transformers of Remote Sensing Image Based on Structural Re-Parameterization

Abstract

In recent times, remote sensing image super-resolution reconstruction technology based on deep learning has experienced rapid development. However, most algorithms in this domain concentrate solely on enhancing the super-resolution network’s performance while neglecting the equally crucial aspect of inference speed. In this study, we propose a method for lightweight super-resolution reconstruction of remote sensing images, termed SRRepViT. This approach reduces model parameters and floating-point operations during inference through parameter equivalent transformation. Using the RSSOD remote sensing dataset as our benchmark dataset, we compared the reconstruction performance, inference time, and model size of SRRepViT with other classical methods. Compared to the lightweight model ECBSR, SRRepViT exhibits slightly improved reconstruction performance while reducing inference time by 16% and model parameters by 34%, respectively. Moreover, compared to other classical super-resolution reconstruction methods, the SRRepViT model achieves similar reconstruction performance while reducing model parameters by 98% and increasing inference speed by 90% for a single remote sensing image.