工矿自动化2025，Vol.51Issue(2)：57-64,99,9.DOI:10.13272/j.issn.1671-251x.2024110072
基于改进Zero-DCE模型的矿井低照度图像增强方法
Low-light image enhancement method for underground mines based on an improved Zero-DCE model
摘要
Abstract
Underground coal mine surveillance images suffer from noise,low clarity,missing color,and texture information.Additionally,machine learning-based image enhancement methods face challenges in collecting paired low-light and normal-light image datasets.To address these issues,this paper proposes an improved Zero-Reference Deep Curve Estimation(Zero-DCE)model for enhancing low-light images in mines.The ReLU activation function in the Zero-DCE model was replaced with Leaky ReLU to accelerate model convergence and improve the efficiency of low-light image feature learning.A Convolutional Block Attention Module(CBAM)was introduced at the skip connections between the shallow and deep networks of the Zero-DCE model to enhance the model's ability to capture key image features.An Asymmetric Convolution Block(ACB)was incorporated into the shallow network to optimize the model's learning of local image features and its ability to represent fine details.A Cascaded Convolution Kernel(CCK)was employed in the deep network to reduce the number of model parameters and computational cost,thereby shortening the training time.Experimental validation was conducted using the LOL public dataset and a self-built mine dataset.The results showed that the improved Zero-DCE model outperformed typical image enhancement models in terms of Mean Squared Error(MSE),Peak Signal-to-Noise Ratio(PSNR),Structural Similarity(SSIM),Natural Image Quality Evaluator(NIQE),and Visual Information Fidelity(VIF).Specifically,on the self-built dataset,MSE and NIQE decreased by 16.25%and 2.93%,respectively,while PSNR,SSIM,and VIF increased by 2.87%,1.87%,and 17.64%,respectively.The enhanced images exhibited superior visual quality,effectively improving brightness while preserving detailed texture information and significantly reducing noise.The inference time for a single image was 0.138 seconds,enabling real-time image enhancement.关键词
矿井低照度图像/图像增强/零参考深度曲线估计网络/Zero-DCE模型/无监督学习Key words
underground low-light images/image enhancement/Zero-Reference Deep Curve Estimation network/Zero-DCE model/unsupervised learning分类
矿山工程引用本文复制引用
王轶玮,李晓宇,翁智,白凤山..基于改进Zero-DCE模型的矿井低照度图像增强方法[J].工矿自动化,2025,51(2):57-64,99,9.基金项目
国家自然科学基金资助项目(52364017) （52364017）
内蒙古自治区自然科学基金项目(2020MS06024,2023QN05023) （2020MS06024,2023QN05023）
2023年度自治区本级引进高层次人才科研支持项目(12000-15042321) （12000-15042321）
2023年高层次人才科研启动项目(10000-23112101/05). （10000-23112101/05）
