广西科技大学学报2026，Vol.37Issue(3)：67-73,95,8.DOI:10.16375/j.cnki.cn45-1395/t.2026.03.008
一种基于YCbCr色彩空间的无监督学习弱光图像增强器
An unsupervised learning low-light image intensifier based on YCbCr colour space
摘要
Abstract
In recent years,deep learning methods have been widely used in the field of low-light image enhancement and have achieved good results.The main purpose of low-light image enhancement is to restore the visibility of the object of interest.However,traditional methods often overlook issues such as noise and artifacts,resulting in a significant degradation of the quality of the enhanced image.In this paper,an unsupervised learning low-light image enhancer based on YCbCr colour space was proposed.The network divided the low-light image enhancement into two parts.First,a brightness adjustment network(BAN)was designed and the luminance enhancement residual(LER)block was used to adjust the luminance.In addition,the CbCr channels were enhanced through a color recovery network(CRN),which captured detailed information from the chrominance channels and integrated both global and local feature information.Finally,the enhanced luminance and chrominance images were spliced in the channel direction and converted to RGB images through colour space.Experimental results show that the proposed method can better handle various types of low-light images and outperforms most of the existing low-light image enhancement methods.The method proposed in this paper has been extensively experimented on four publicly available datasets and has shown good performance compared with other state-of-the-art competitors in terms of reference/non-reference quality assessment.关键词
YCbCr色彩空间/RGB图像/弱光图像增强/无监督学习Key words
YCbCr colour space/RGB images/low light image enhancement/unsupervised learning分类
信息技术与安全科学引用本文复制引用
丁子扬,谢鹏鹏,李千帆,杜大志,苏美霖..一种基于YCbCr色彩空间的无监督学习弱光图像增强器[J].广西科技大学学报,2026,37(3):67-73,95,8.基金项目
国家自然科学基金项目(52202491)资助 （52202491）
