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{this https URL}{here}.
View on arXiv@article{zhao2025_2503.13165,
title={ From Zero to Detail: Deconstructing Ultra-High-Definition Image Restoration from Progressive Spectral Perspective },
author={ Chen Zhao and Zhizhou Chen and Yunzhe Xu and Enxuan Gu and Jian Li and Zili Yi and Qian Wang and Jian Yang and Ying Tai },
journal={arXiv preprint arXiv:2503.13165},
year={ 2025 }
}