@article{TANG2026, 
author = {Jian TANG and Wengang CHE and Shengxiang GAO},
title = {An improved lightweight network for image dehazing},
year = {2026},
journal = {Journal of Chongqing University},
volume = {49},
number = {6},
pages = {71-81},
keywords = {image dehazing, lightweight network, attention mechanism, reverse residual network},
url = {https://www.sciopen.com/article/10.11835/j.issn.1000-582X.2026.06.007},
doi = {10.11835/j.issn.1000-582X.2026.06.007},
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 realworld images. In addition, the model exhibits reduced parameter size and improved computational efficiency.}
}