重庆大学学报2025，Vol.48Issue(2)：50-61,12.DOI:10.11835/j.issn.1000-582X.2025.02.005
基于改进多尺度AOD-Net的图像去雾算法
An image dehazing algorithm based on improved multi-scale AOD-Net
摘要
Abstract
To address the current issues of inefficient dehazing algorithms and poor detail recovery,we propose an improved multi-scale AOD-Net(all in one dehazing network)algorithm.This algorithm enhances the network's feature extraction and recovery capabilities through three key improvements:adding an attention mechanism,adjusting the network structure,and modifying the loss function.Specifically,the first layer of the model incorporates the SPA(spatial pyramid attention)mechanism,which enables the network to avoid redundant information during feature extraction.Furthermore,the network structure is modified into a Laplacian pyramid structure,allowing the model to extract features at different scales and preserve high-frequency information in the feature maps.Additionally,the original loss function is replaced with the MS-SSIM(multi-scale structural similarity)+L1 loss function,thereby enhancing the model's ability to retain structural information.Experimental results demonstrate that this method achieves better dehazing effects and richer details.Subjectively,the dehazed images exhibit superior quality compared to those produced by the original network.Objectively,compared to the original network,there is a 2.55 dB improvement in PSNR,a 0.04 increase in SSIM value,and a 0.18 increase in IE entropy value,which proves the algorithm's excellent dehazing effect and stability.关键词
去雾处理/AOD-Net/注意力机制/拉普拉斯金字塔/损失函数Key words
dehazing/AOD-Net/attention mechanism/Laplacian pyramid/loss function分类
计算机与自动化引用本文复制引用
徐玥,黄志开,王欢,曾志超,王景玉,叶元龙..基于改进多尺度AOD-Net的图像去雾算法[J].重庆大学学报,2025,48(2):50-61,12.基金项目
国家重点研发计划(2019YFB1704502) （2019YFB1704502）
国家自然科学基金(61472173) （61472173）
江西省研究生创新专项资金(yc2023-s995,YJSCX202312).Supported by the National Key R&D Program of China(2019YFB1704502),Natinal Natural Science Foundation of China(61472173),and Jiangxi Province Graduate Innovation Special Fund(yc2023-s995,YJSCX202312). （yc2023-s995,YJSCX202312）
