Lightweight Multi‐Stage Holistic Attention‐Based Network for Image Super‐Resolution

Abstract

ABSTRACT High‐resolution images are crucial for many applications, but factors such as environmental conditions can reduce image quality. Super‐resolution (SR) techniques address this by generating high‐resolution images from low‐resolution inputs. While deep learning SR models have made significant progress, they can be computationally expensive and struggle with differentiating between various image scales. Lightweight SR methods, suitable for resource‐constrained devices, often compromise image quality. This study introduces a multi‐stage holistic attention‐based network, using Gaussian Laplacian pyramids to decompose images and apply holistic attention modules at each level. This approach reduces parameters and computational costs while maintaining image quality, achieving a PSNR score of 28 and SSIM of 0.91 with only 29,000 parameters. The model demonstrates the potential for efficient and high‐quality image reconstruction. Future work will focus on improving quality while minimizing costs and exploring other advanced techniques. The code will be made available upon request