Currently, to further improve visual enjoyment, Ultra-High-Definition (UHD)
images are catching wide attention. Here, UHD images are usually referred to as
having a resolution greater than or equal to $3840 \times 2160$. However, since
the imaging equipment is subject to environmental noise or equipment jitter,
UHD images are prone to contrast degradation, blurring, low dynamic range, etc.
To address these issues, a large number of algorithms for UHD image enhancement
have been proposed. In this paper, we introduce the current state of UHD image
enhancement from two perspectives, one is the application field and the other
is the technology. In addition, we briefly explore its trends.

本文介绍了超高清图像的增强技术和应用领域，并对其未来发展趋势进行了简要探讨。

超高分辨率图像处理的进展与趋势概述

Advancements and Trends in Ultra-High-Resolution Image Processing: An  Overview

With the rapid development of mobile devices, modern widely-used mobile
phones typically allow users to capture 4K resolution (i.e.,
ultra-high-definition) images. However, for image demoireing, a challenging
task in low-level vision, existing works are generally carried out on
low-resolution or synthetic images. Hence, the effectiveness of these methods
on 4K resolution images is still unknown. In this paper, we explore moire
pattern removal for ultra-high-definition images. To this end, we propose the
first ultra-high-definition demoireing dataset (UHDM), which contains 5,000
real-world 4K resolution image pairs, and conduct a benchmark study on current
state-of-the-art methods. Further, we present an efficient baseline model
ESDNet for tackling 4K moire images, wherein we build a semantic-aligned
scale-aware module to address the scale variation of moire patterns. Extensive
experiments manifest the effectiveness of our approach, which outperforms
state-of-the-art methods by a large margin while being much more lightweight.
Code and dataset are available at this https URL

本文提出了一种针对超高清图像去莫尔纹的方法，首次建立了 UHDM 数据集并在其中进行了基准研究，提出了一种有效的基准模型 ESDNet，其中包括语义对齐的尺度感知模块以解决莫尔纹图案的尺度变化问题，实验证明该方法在性能和效率方面优于当前领先的方法。