聊城大学学报（自然科学版）2026，Vol.39Issue(3)：357-365,9.DOI:10.19728/j.issn1672-6634.2025040002
联合物理模型和先验的无监督低照度图像增强
Unsupervised low-light image enhancement with joint physical modeling and a priori
摘要
Abstract
The training and enhancement effect of supervised learning relies on paired datasets,but most of the data sets are synthetic datasets,which are poorly recovered and weakly generalized when migrated to real images.Based on the above problems,this paper proposes an unsupervised low-light image enhance-ment model with a joint physical model and multiple priors.First,supervised pre-training is performed on the synthetic dataset to meet the demand of the model to the low illumination;second,the model is fur-ther unsupervised,trained,and optimized on the real dataset and jointly with multiple physical a priori knowledge to better adapt it to the actual low illumination situation.The experiments show that the de-tails and illumination of the images are restored while the dependence on paired datasets is somewhat elimi-nated.关键词
图像处理/低照度图像增强/无监督学习/物理模型/先验知识Key words
image processing/low-light image enhancement/unsupervised learning/physical modeling/physical priori分类
信息技术与安全科学引用本文复制引用
刘凌风,梁玉雪,扈成双,张祯,刘鉴朗,邵凯鑫,陈勇..联合物理模型和先验的无监督低照度图像增强[J].聊城大学学报（自然科学版）,2026,39(3):357-365,9.基金项目
国家自然科学基金项目(51977021) （51977021）
重庆邮电大学大学生科研训练计划(A2023-214)资助 （A2023-214）
