Exploring Linear Attention Alternative for Single Image Super-Resolution
This work addresses image quality and efficiency challenges in super-resolution for applications like remote sensing, but it appears incremental with small performance gains.
The paper tackled the problem of computational complexity and quality in single-image super-resolution, particularly for remote sensing images, by proposing the OmniRWKVSR model, which achieved an average improvement of 0.26% in PSNR and 0.16% in SSIM compared to the MambaIR model under 4x super-resolution tasks.
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.