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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2503.13165 |
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| _version_ | 1866913740803276800 |
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| author | Zhao, Chen Chen, Zhizhou Xu, Yunzhe Gu, Enxuan Li, Jian Yi, Zili Wang, Qian Yang, Jian Tai, Ying |
| author_facet | Zhao, Chen Chen, Zhizhou Xu, Yunzhe Gu, Enxuan Li, Jian Yi, Zili Wang, Qian Yang, Jian Tai, Ying |
| contents | 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}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13165 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Zero to Detail: Deconstructing Ultra-High-Definition Image Restoration from Progressive Spectral Perspective Zhao, Chen Chen, Zhizhou Xu, Yunzhe Gu, Enxuan Li, Jian Yi, Zili Wang, Qian Yang, Jian Tai, Ying Computer Vision and Pattern Recognition 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}. |
| title | From Zero to Detail: Deconstructing Ultra-High-Definition Image Restoration from Progressive Spectral Perspective |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.13165 |