Image restoration faces challenges including ineffective feature fusion, computational bottlenecks and inefficient diffusion processes. To address these, we propose DiffRWKVIR, a novel framework unifying Test-Time Training (TTT) with efficient diffusion. Our approach introduces three key innovations: (1) Omni-Scale 2D State Evolution extends RWKV's location-dependent parameterization to hierarchical multi-directional 2D scanning, enabling global contextual awareness with linear complexity O(L); (2) Chunk-Optimized Flash Processing accelerates intra-chunk parallelism by 3.2x via contiguous chunk processing (O(LCd) complexity), reducing sequential dependencies and computational overhead; (3) Prior-Guided Efficient Diffusion extracts a compact Image Prior Representation (IPR) in only 5-20 steps, proving 45% faster training/inference than DiffIR while solving computational inefficiency in denoising. Evaluated across super-resolution and inpainting benchmarks (Set5, Set14, BSD100, Urban100, Places365), DiffRWKVIR outperforms SwinIR, HAT, and MambaIR/v2 in PSNR, SSIM, LPIPS, and efficiency metrics. Our method establishes a new paradigm for adaptive, high-efficiency image restoration with optimized hardware utilization.

翻译：图像复原面临特征融合效果不佳、计算瓶颈及扩散过程效率低下等挑战。为解决这些问题，我们提出DiffRWKVIR——一种将测试时训练（TTT）与高效扩散相统一的新型框架。该方法包含三项关键创新：（1）全尺度二维状态演化：将RWKV的位置依赖参数化扩展至分层多方向二维扫描，实现具有线性复杂度O(L)的全局上下文感知；（2）分块优化闪存处理：通过连续分块处理（复杂度O(LCd)）将块内并行计算加速3.2倍，减少序列依赖性与计算开销；（3）先验引导高效扩散：仅需5-20步即可提取紧凑的图像先验表示（IPR），在解决去噪计算低效问题的同时，训练/推理速度较DiffIR提升45%。在超分辨率与图像修复基准测试（Set5、Set14、BSD100、Urban100、Places365）中，DiffRWKVIR在PSNR、SSIM、LPIPS及效率指标上均优于SwinIR、HAT和MambaIR/v2。本方法为自适应、高效率的图像复原建立了硬件利用率优化的新范式。