LSwinSR: UAV Imagery Super-Resolution based on Linear Swin Transformer

TL;DR

This paper introduces LSwinSR, a super-resolution network based on the Swin Transformer, designed to enhance UAV imagery quality efficiently and accurately, with evaluation based on both image quality metrics and semantic segmentation performance.

Contribution

Proposes a novel Swin Transformer-based super-resolution network for UAV images, improving efficiency and competitive accuracy over existing methods.

Findings

Enhanced image resolution for UAV imagery.

Improved semantic segmentation accuracy after super-resolution.

Code availability for reproducibility.

Abstract

Super-resolution, which aims to reconstruct high-resolution images from low-resolution images, has drawn considerable attention and has been intensively studied in computer vision and remote sensing communities. The super-resolution technology is especially beneficial for Unmanned Aerial Vehicles (UAV), as the amount and resolution of images captured by UAV are highly limited by physical constraints such as flight altitude and load capacity. In the wake of the successful application of deep learning methods in the super-resolution task, in recent years, a series of super-resolution algorithms have been developed. In this paper, for the super-resolution of UAV images, a novel network based on the state-of-the-art Swin Transformer is proposed with better efficiency and competitive accuracy. Meanwhile, as one of the essential applications of the UAV is land cover and land use monitoring, simple image quality assessments such as the Peak-Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index Measure (SSIM) are not enough to comprehensively measure the performance of an algorithm. Therefore, we further investigate the effectiveness of super-resolution methods using the accuracy of semantic segmentation. The code will be available at https://github.com/lironui/LSwinSR.