Towards Ultra-High-Definition Image Deraining: A Benchmark and An Efficient Method
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914813424173056 |
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| author | Chen, Hongming Chen, Xiang Wu, Chen Zheng, Zhuoran Pan, Jinshan Fu, Xianping |
| author_facet | Chen, Hongming Chen, Xiang Wu, Chen Zheng, Zhuoran Pan, Jinshan Fu, Xianping |
| contents | Despite significant progress has been made in image deraining, existing approaches are mostly carried out on low-resolution images. The effectiveness of these methods on high-resolution images is still unknown, especially for ultra-high-definition (UHD) images, given the continuous advancement of imaging devices. In this paper, we focus on the task of UHD image deraining, and contribute the first large-scale UHD image deraining dataset, 4K-Rain13k, that contains 13,000 image pairs at 4K resolution. Based on this dataset, we conduct a benchmark study on existing methods for processing UHD images. Furthermore, we develop an effective and efficient vision MLP-based architecture (UDR-Mixer) to better solve this task. Specifically, our method contains two building components: a spatial feature rearrangement layer that captures long-range information of UHD images, and a frequency feature modulation layer that facilitates high-quality UHD image reconstruction. Extensive experimental results demonstrate that our method performs favorably against the state-of-the-art approaches while maintaining a lower model complexity. The code and dataset will be available at https://github.com/cschenxiang/UDR-Mixer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17074 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Ultra-High-Definition Image Deraining: A Benchmark and An Efficient Method Chen, Hongming Chen, Xiang Wu, Chen Zheng, Zhuoran Pan, Jinshan Fu, Xianping Computer Vision and Pattern Recognition Despite significant progress has been made in image deraining, existing approaches are mostly carried out on low-resolution images. The effectiveness of these methods on high-resolution images is still unknown, especially for ultra-high-definition (UHD) images, given the continuous advancement of imaging devices. In this paper, we focus on the task of UHD image deraining, and contribute the first large-scale UHD image deraining dataset, 4K-Rain13k, that contains 13,000 image pairs at 4K resolution. Based on this dataset, we conduct a benchmark study on existing methods for processing UHD images. Furthermore, we develop an effective and efficient vision MLP-based architecture (UDR-Mixer) to better solve this task. Specifically, our method contains two building components: a spatial feature rearrangement layer that captures long-range information of UHD images, and a frequency feature modulation layer that facilitates high-quality UHD image reconstruction. Extensive experimental results demonstrate that our method performs favorably against the state-of-the-art approaches while maintaining a lower model complexity. The code and dataset will be available at https://github.com/cschenxiang/UDR-Mixer. |
| title | Towards Ultra-High-Definition Image Deraining: A Benchmark and An Efficient Method |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.17074 |