重庆大学学报2026，Vol.49Issue(6)：71-81,11.DOI:10.11835/j.issn.1000-582X.2026.06.007
一种改进的轻量型网络图像去雾方法
An improved lightweight network for image dehazing
摘要
Abstract
To address the issues of high computational complexity and large parameter size in convolutional neural network(CNN)-based image dehazing,this study proposes a lightweight dehazing network(LDNet).First,the atmospheric scattering model is reformulated to directly suppress haze noise,thereby reducing cumulative errors in intermediate variable estimation.Second,a reverse residual network module with an attention mechanism(RNAM)is designed to extract multi-scale features while emphasizing critical semantic information,effectively reducing model complexity and parameter size.Finally,a joint loss function combining L1 smoothing loss and multi-scale structure similarity(MS-SSIM)loss is used to improve reconstruction quality.The experimental results show that the proposed method outperforms existing approaches in terms of structural similarity and peak signal-to-noise ratio(PSNR)on synthetic datasets,while also achieving effective dehazing performance on real-world images.In addition,the model exhibits reduced parameter size and improved computational efficiency.关键词
图像去雾/轻量型网络/注意力机制/倒残差网络Key words
image dehazing/lightweight network/attention mechanism/reverse residual network分类
信息技术与安全科学引用本文复制引用
唐剑,车文刚,高盛祥..一种改进的轻量型网络图像去雾方法[J].重庆大学学报,2026,49(6):71-81,11.基金项目
国家自然科学基金(61972186) （61972186）
云南省重大科技专项计划(202103AA080015).Supported by National Natural Science Foundation of China(61972186),and Major Science and Technology Special Project of Yunnan Province(202103AA080015). （202103AA080015）
