Towards Ultra-High-Definition Image Deraining: A Benchmark and An Efficient Method
<|reference_start|>Towards Ultra-High-Definition Image Deraining: A Benchmark and An Efficient Method: 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.<|reference_end|>
@article{chen2024towards,
    title={Towards Ultra-High-Definition Image Deraining: A Benchmark and An
  Efficient Method},
    author={Hongming Chen, Xiang Chen, Chen Wu, Zhuoran Zheng, Jinshan Pan,
  Xianping Fu},
    journal={arXiv preprint arXiv:2405.17074},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17074},
    primaryClass={cs.CV}
}