分三阶段逐步修复超高清图像，细节更真实。
From Zero to Detail: Deconstructing Ultra-High-Definition Image Restoration from Progressive Spectral Perspective
- 按频谱从低到高分三步：全局增强、粗结构恢复、细节精修。
- 在UHD图像修复任务中，各项指标显著优于现有方法。
- 适合追求超清图像还原的视觉算法研究者使用。
超高清（UHD）图像修复因分辨率高、内容复杂、细节丰富而面临巨大挑战。本文通过渐进式频谱视角深入分析修复过程，将复杂的UHD修复问题解构为三个渐进阶段：零频增强、低频恢复和高频精修。基于此洞察，提出新型框架ERR，包含三个协同子网络：零频增强器（ZFE）、低频恢复器（LFR）和高频精修器（HFR）。其中，ZFE融合全局先验以学习全局映射，LFR专注于粗粒度内容重建，而HFR采用设计的频率窗式Kolmogorov-Arnold网络（FW-KAN）来精细修复纹理与细节，生成高质量复原结果。大量消融实验验证了各组件的有效性。代码已开源。
原文摘要 · Abstract (English)
Ultra-high-definition (UHD) image restoration faces significant challenges due to its high resolution, complex content, and intricate details. To cope with these challenges, we analyze the restoration process in depth through a progressive spectral perspective, and deconstruct the complex UHD restoration problem into three progressive stages: zero-frequency enhancement, low-frequency restoration, and high-frequency refinement. Building on this insight, we propose a novel framework, ERR, which comprises three collaborative sub-networks: the zero-frequency enhancer (ZFE), the low-frequency restorer (LFR), and the high-frequency refiner (HFR). Specifically, the ZFE integrates global priors to learn global mapping, while the LFR restores low-frequency information, emphasizing reconstruction of coarse-grained content. Finally, the HFR employs our designed frequency-windowed kolmogorov-arnold networks (FW-KAN) to refine textures and details, producing high-quality image restoration. Our approach significantly outperforms previous UHD methods across various tasks, with extensive ablation studies validating the effectiveness of each component. The code is available at \href{https://github.com/NJU-PCALab/ERR}{here}.
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