用新型RWKV架构提升图像超分质量与效率
Exploring Linear Attention Alternative for Single Image Super-Resolution
- 引入RWKV结构结合空间与通道混合模块
- 4倍超分任务下PSNR提升0.26%，SSIM提升0.16%
- 适合遥感图像等高精度重建场景
基于深度学习的单图像超分辨率（SISR）技术致力于将低分辨率（LR）图像增强为高分辨率（HR）图像。尽管已有显著进展，但在计算复杂度和图像质量方面仍面临挑战，尤其在遥感图像处理中更为突出。为此，我们提出Omni-Scale RWKV Super-Resolution（OmniRWKVSR）模型，创新性地结合了接收权重键值（RWKV）架构与视觉空间混合（VRSM）和视觉通道混合（VRCM）特征提取技术，旨在克服现有方法的局限，实现更优的SISR性能。实验表明，在4倍超分任务中，相比MambaIR模型，本方法在PSNR上平均提升0.26%，在SSIM上提升0.16%，有效提升了高质量图像重建能力。
原文摘要 · Abstract (English)
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.
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