  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.
