Ultra-High-Definition (UHD) photo has gradually become the standard configuration in advanced imaging devices. The new standard unveils many issues in existing approaches for low-light image enhancement (LLIE), especially in dealing with the intricate issue of joint luminance enhancement and noise removal while remaining efficient. Unlike existing methods that address the problem in the spatial domain, we propose a new solution, UHDFour, that embeds Fourier transform into a cascaded network. Our approach is motivated by a few unique characteristics in the Fourier domain: 1) most luminance information concentrates on amplitudes while noise is closely related to phases, and 2) a high-resolution image and its low-resolution version share similar amplitude patterns.Through embedding Fourier into our network, the amplitude and phase of a low-light image are separately processed to avoid amplifying noise when enhancing luminance. Besides, UHDFour is scalable to UHD images by implementing amplitude and phase enhancement under the low-resolution regime and then adjusting the high-resolution scale with few computations. We also contribute the first real UHD LLIE dataset, \textbf{UHD-LL}, that contains 2,150 low-noise/normal-clear 4K image pairs with diverse darkness and noise levels captured in different scenarios. With this dataset, we systematically analyze the performance of existing LLIE methods for processing UHD images and demonstrate the advantage of our solution. We believe our new framework, coupled with the dataset, would push the frontier of LLIE towards UHD. The code and dataset are available at https://li-chongyi.github.io/UHDFour.

翻译：超高清（UHD）照片已逐渐成为先进成像设备的标准配置。这一新标准揭示了现有低光图像增强（LLIE）方法中的许多问题，尤其是在兼顾亮度提升与噪声去除的复杂问题中保持高效性。与现有在空间域处理问题的方法不同，我们提出了一种名为UHDFour的新解决方案，将傅里叶变换嵌入级联网络。我们的方法基于傅里叶域的几个独特特性：1）大部分亮度信息集中于振幅，而噪声与相位密切相关；2）高分辨率图像与其低分辨率版本具有相似的振幅模式。通过将傅里叶变换嵌入网络，低光图像的振幅和相位被分别处理，从而在增强亮度时避免放大噪声。此外，UHDFour通过低分辨率下的振幅和相位增强，并以少量计算调整高分辨率尺度，从而可扩展至UHD图像。我们还贡献了首个真实的UHD LLIE数据集——\textbf{UHD-LL}，包含2150对不同场景下拍摄的低噪声/正常清晰4K图像对，具有多样化的黑暗程度和噪声水平。利用该数据集，我们系统分析了现有LLIE方法处理UHD图像的性能，并展示了我们解决方案的优势。我们相信，这一结合数据集的新框架将推动LLIE向UHD领域的前沿发展。代码和数据集已公开于https://li-chongyi.github.io/UHDFour。