Exploring Linear Attention Alternative for Single Image Super-Resolution

TL;DR

This paper introduces OmniRWKVSR, a novel super-resolution model combining RWKV architecture with feature extraction techniques, achieving improved image quality in remote sensing applications with reduced computational complexity.

Contribution

The paper presents a new super-resolution model that integrates RWKV architecture with specialized feature extraction methods for enhanced performance.

Findings

Achieved 0.26% higher PSNR compared to MambaIR.

Achieved 0.16% higher SSIM compared to MambaIR.

Effective in remote sensing image super-resolution.

Abstract

Deep learning-based single-image super-resolution (SISR) technology focuses on enhancing low-resolution (LR) images into high-resolution (HR) ones. Although significant progress has been made, challenges remain in computational complexity and quality, particularly in remote sensing image processing. To address these issues, we propose our Omni-Scale RWKV Super-Resolution (OmniRWKVSR) model which presents a novel approach that combines the Receptance Weighted Key Value (RWKV) architecture with feature extraction techniques such as Visual RWKV Spatial Mixing (VRSM) and Visual RWKV Channel Mixing (VRCM), aiming to overcome the limitations of existing methods and achieve superior SISR performance. This work has proved able to provide effective solutions for high-quality image reconstruction. Under the 4x Super-Resolution tasks, compared to the MambaIR model, we achieved an average improvement of 0.26% in PSNR and 0.16% in SSIM.