计算机应用研究2025，Vol.42Issue(1)：19-27,9.DOI:10.19734/j.issn.1001-3695.2024.06.0176
基于深度学习的低光照图像增强研究综述
Review of low light image enhancement based on deep learning
摘要
Abstract
The aim of low-light image enhancement is to optimize images captured in low-light environments by improving their brightness and contrast.Currently,deep learning has become the main method in the field of low-light image enhancement,necessitating a review of deep learning-based methods.First,this paper classified traditional methods of low-light image en-hancement and analyzed and summarized their advantages and disadvantages.Then,this paper focused on deep learning-based methods,classified them into supervised and unsupervised categories,and summarized their respective advantages and disad-vantages.This paper also summarized the loss functions applied in deep learning approaches.Next,this paper briefly summa-rized the commonly used datasets and evaluation metrics,using information entropy to quantitatively compare traditional me-thods,and employing peak signal-to-noise ratio and structural similarity to objectively evaluate deep learning-based methods.Finally,this paper summarized the shortcomings of current methods and prospect future research directions.关键词
低光照图像增强/深度学习/有监督/特征提取/无监督Key words
low-light image enhancement/deep learning/supervised/feature extraction/unsupervised分类
计算机与自动化引用本文复制引用
孙福艳,吕准,吕宗旺..基于深度学习的低光照图像增强研究综述[J].计算机应用研究,2025,42(1):19-27,9.基金项目
国家重点研发计划资助项目(2022YFD2100202) （2022YFD2100202）
