重庆理工大学学报2024，Vol.38Issue(1)：122-130,9.DOI:10.3969/j.issn.1674-8425(z).2024.01.014
隧道场景下行人检测DA-Zero-DCE图像增强算法
A denoising-attention based Zero-DCE for tunnel image enhancement
摘要
Abstract
Tunnel images,affected by the shooting environment,suffer from uneven illumination distribution,local occlusion,and many noises.To address the problems of overexposure and distortion in existing image enhancement algorithms,this paper proposes a tunnel image enhancement algorithm called DA-Zero-DCE(Denoising-Attention based Zero-Reference Deep Curve Estimation).First,based on the Zero-DCE model,the U-Net is employed to improve the backbone network DCE-Net for curve estimation,and a coordinate attention mechanism is added to enhance the dark light perception ability of local image areas.Second,the NAF-Net noise removal module is added after the curve estimation backbone network to effectively suppress the noises after low-light enhancement by Zero-DCE.To offset the distortion and overexposure of the enhanced images,the 4-neighborhood calculation method of the spatial consistency loss function is extended to an 8-neighborhood calculation method,enhancing the smoothness of the outputs.Through the ablation experiment on the LOL dataset,the DA-Zero-DCE model,compared to the Zero-DCE model,improves PSNR by 10 dB and SSIM by 0.1,demonstrating its feasibility and effectiveness.关键词
深度学习/卷积神经网络/计算机视觉/图像增强Key words
deep learning/convolutional neural network/computer vision/tunnel image enhance-ment分类
交通工程引用本文复制引用
周桐,李冬春,田雨聃..隧道场景下行人检测DA-Zero-DCE图像增强算法[J].重庆理工大学学报,2024,38(1):122-130,9.基金项目
中国高校产学研创新基金项目(2021BCG02002) （2021BCG02002）
重庆市教委科学技术研究计划项目(KJZD-K202303405,KJQN202303423) （KJZD-K202303405,KJQN202303423）
重庆英才计划"包干制"项目(cstc2022ycjh-bgzxm0108) （cstc2022ycjh-bgzxm0108）
