  Currently, mobile and IoT devices are in dire need of a series of methods to
enhance 4K images with limited resource expenditure. The absence of large-scale
4K benchmark datasets hampers progress in this area, especially for dehazing.
The challenges in building ultra-high-definition (UHD) dehazing datasets are
the absence of estimation methods for UHD depth maps, high-quality 4K depth
estimation datasets, and migration strategies for UHD haze images from
synthetic to real domains. To address these problems, we develop a novel
synthetic method to simulate 4K hazy images (including nighttime and daytime
scenes) from clear images, which first estimates the scene depth, simulates the
light rays and object reflectance, then migrates the synthetic images to real
domains by using a GAN, and finally yields the hazy effects on 4K resolution
images. We wrap these synthesized images into a benchmark called the 4K-HAZE
dataset. Specifically, we design the CS-Mixer (an MLP-based model that
integrates \textbf{C}hannel domain and \textbf{S}patial domain) to estimate the
depth map of 4K clear images, the GU-Net to migrate a 4K synthetic image to the
real hazy domain. The most appealing aspect of our approach (depth estimation
and domain migration) is the capability to run a 4K image on a single GPU with
24G RAM in real-time (33fps). Additionally, this work presents an objective
assessment of several state-of-the-art single-image dehazing methods that are
evaluated using the 4K-HAZE dataset. At the end of the paper, we discuss the
limitations of the 4K-HAZE dataset and its social implications.
