Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement

TL;DR

This paper introduces UHDFour, a Fourier-based neural network for efficient ultra-high-definition low-light image enhancement, and provides the first UHD LLIE dataset to evaluate and advance the field.

Contribution

The paper proposes a novel Fourier embedding approach for UHD low-light image enhancement and introduces the first real UHD LLIE dataset, enabling systematic evaluation.

Findings

UHDFour effectively separates luminance and noise in Fourier domain.

The method scales efficiently to UHD images with low computational cost.

Experimental results show superior performance over existing LLIE methods.

Abstract

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.