Ultra-High-Definition (UHD) image restoration has acquired remarkable
attention due to its practical demand. In this paper, we construct UHD snow and
rain benchmarks, named UHD-Snow and UHD-Rain, to remedy the deficiency in this
field. The UHD-Snow/UHD-Rain is established by simulating the physics process
of rain/snow into consideration and each benchmark contains 3200 degraded/clear
image pairs of 4K resolution. Furthermore, we propose an effective UHD image
restoration solution by considering gradient and normal priors in model design
thanks to these priors' spatial and detail contributions. Specifically, our
method contains two branches: (a) feature fusion and reconstruction branch in
high-resolution space and (b) prior feature interaction branch in
low-resolution space. The former learns high-resolution features and fuses
prior-guided low-resolution features to reconstruct clear images, while the
latter utilizes normal and gradient priors to mine useful spatial features and
detail features to guide high-resolution recovery better. To better utilize
these priors, we introduce single prior feature interaction and dual prior
feature interaction, where the former respectively fuses normal and gradient
priors with high-resolution features to enhance prior ones, while the latter
calculates the similarity between enhanced prior ones and further exploits dual
guided filtering to boost the feature interaction of dual priors. We conduct
experiments on both new and existing public datasets and demonstrate the
state-of-the-art performance of our method on UHD image low-light enhancement,
UHD image desonwing, and UHD image deraining. The source codes and benchmarks
are available at https://github.com/wlydlut/UHDDIP.

本文构建了 UHD-Snow 和 UHD-Rain 两个基准数据集，模拟了雨雪的物理过程，研究提出了一种考虑梯度和法线先验的 UHD 图像恢复解决方案，包括高分辨率空间的特征融合与重构分支以及低分辨率空间的先验特征交互分支。实验证明了该方法在 UHD 图像低光增强、去噪和除雨方面具有领先的性能。