A two-branch feature joint dehazing network based on multidimensional collaborative attention

doi:10.3785/j.issn.1008-973X.2025.06.003

Journal of ZheJiang University (Engineering Science)

2025, Vol. 59

Issue (6): 1119-1129 DOI: 10.3785/j.issn.1008-973X.2025.06.003

A two-branch feature joint dehazing network based on multidimensional collaborative attention

Yan YANG(

),Lipeng CHAO

School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

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Abstract

The dehazing task was divided into two subtasks, content information extraction and edge feature restoration, and a two-branch feature joint dehazing network based on multidimensional collaborative attention was proposed, aiming at the problems that the single-feature extraction network had difficulty in collaboratively enhancing content and edge features in hazy image restoration. In the first branch of the network, densely connected convolutional blocks were constructed to extract multi-level content information from hazy images. In the second branch, cascaded multi-scale residual convolutional blocks were used to restore the texture details. Then the image reconstruction module was used to perform the multi-scale reconstruction on the two types of features, achieving the exchange and enhancement of different feature information, and improving the dehazing effect. Attention mechanism was introduced into the network and attention interactions were modeled in space and pixels to efficiently learn the main features of hazy images. The experimental results show that the proposed network outperforms most existing algorithms in objective metrics on multiple datasets, with high content restoration and complete texture details in dehazing visual effects.

Key words： image dehazing image restoration neural network multidimensional collaborative attention feature reconstruction

Received: 02 April 2024 Published: 30 May 2025

CLC:

TP 391.4

Fund: 国家自然科学基金资助项目（61561030，62063014）；甘肃省高等学校产业支撑计划资助项目（2021CYZC-04）；兰州交通大学研究生教改项目（JG201928）.

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Cite this article:

Yan YANG,Lipeng CHAO. A two-branch feature joint dehazing network based on multidimensional collaborative attention. Journal of ZheJiang University (Engineering Science), 2025, 59(6): 1119-1129.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.06.003 OR https://www.zjujournals.com/eng/Y2025/V59/I6/1119

基于多维协同注意力的双支特征联合去雾网络

针对雾天图像复原中单一特征提取网络难以协同增强内容与边缘特征的问题，将去雾任务分为内容信息提取和边缘特征恢复2个子任务，提出基于多维协同注意力的双支特征联合去雾网络. 在第1个分支构建密集连接的卷积块提取有雾图像中的多层次内容信息；在第2个分支采用级联的多尺度残差卷积块对图像的纹理细节进行恢复；通过图像重构模块对2种特征进行多尺度重构，实现不同特征信息的交换和增强，提升去雾效果. 在网络中引入注意力机制，同时在空间和像素上进行注意力交互建模，使网络能够高效率地学习有雾图像的主要特征. 实验结果表明，所提网络在多个数据集上的客观指标均优于大多数现有算法的；在去雾视觉效果上，所提网络能够实现高内容还原度，并完整保留纹理细节.

关键词： 图像去雾, 图像恢复, 神经网络, 多维协同注意力, 特征重建

Fig.1 Framework of two-branch feature joint dehazing network based on multidimensional collaborative attention

Fig.2 Structure of dilated residual dense block （DRDB）

Fig.3 Structure of multidimensional collaborative attention（MCA）

Fig.4 Structure of cascaded convolutional block（CCB）

Fig.5 Structure of multi-scale feature extraction block (MFEB)

Tab.1 Results of ablation experiments on different modules of proposed method

Fig.6 Comparison of local magnification of dehazing effect of ablation experiments

Tab.2 Results of ablation experiments with different edge extraction operators

Fig.7 Dehazing effect with different edge feature operators

Fig.8 Comparison of dehazing effect of different algorithms on SOTS-outdoor

Fig.9 Comparison of dehazing effect of different algorithms on SOTS-indoor

Fig.10 Comparison of dehazing effect of different algorithms on Haze-RD

Fig.11 Comparison of dehazing effect of different algorithms on real hazy images

Fig.12 Comparison of edge details of dehazing effect of proposed model

Tab.3 Comparison of objective evaluations of different methods

Tab.4 Comparison of dehazing time and model complexity of different methods


[1]	吴迪, 朱青松图像去雾的最新研究进展[J]. 自动化学报, 2015, 41 (2): 221- 239 WU Di, ZHU Qingsong The latest research progress of image dehazing[J]. Acta Automatica Sinica, 2015, 41 (2): 221- 239

[2]	TAREL J P, HAUTIÈRE N. Fast visibility restoration from a single color or gray level image [C]// IEEE 12th International Conference on Computer Vision. Kyoto: IEEE, 2009: 2201–2208.

[3]	MENG G, WANG Y, DUAN J, et al. Efficient image dehazing with boundary constraint and contextual regularization [C]// IEEE International Conference on Computer Vision. Sydney: IEEE, 2013: 617–624.

[4]	覃宏超, 李炎炎, 龙伟, 等基于引导滤波和暗原色先验理论透射率估值的视频去雾算法[J]. 浙江大学学报: 工学版, 2018, 52 (7): 1302- 1309 QIN Hongchao, LI Yanyan, LONG Wei, et al Real-time video dehazing using guided filtering and transmissivity estimated based on dark channel prior theory[J]. Journal of Zhejiang University: Engineering Science, 2018, 52 (7): 1302- 1309

[5]	NAYAR S K, NARASIMHAN S G. Vision in bad weather [C]// 7th IEEE International Conference on Computer Vision. Kerkyra: IEEE, 1999: 820–827.

[6]	NARASIMHAN S G, NAYAR S K. Removing weather effects from monochrome images [C]// IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Kauai: IEEE, 2001: II.

[7]	LIHE Z, HE J, YUAN Q, et al PhDnet: a novel physic-aware dehazing network for remote sensing images[J]. Information Fusion, 2024, 106: 102277 doi: 10.1016/j.inffus.2024.102277

[8]	HE K, SUN J, TANG X. Single image haze removal using dark channel prior [C]// IEEE Conference on Computer Vision and Pattern Recognition. Miami: IEEE, 2009: 1956–1963.

[9]	ZHU Q, MAI J, SHAO L A fast single image haze removal algorithm using color attenuation prior[J]. IEEE Transactions on Image Processing, 2015, 24 (11): 3522- 3533 doi: 10.1109/TIP.2015.2446191

[10]	CAI B, XU X, JIA K, et al DehazeNet: an end-to-end system for single image haze removal[J]. IEEE Transactions on Image Processing, 2016, 25 (11): 5187- 5198

[11]	LI B, PENG X, WANG Z, et al. AOD-net: all-in-one dehazing network [C]// IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 4780–4788.

[12]	HE K, SUN J, TANG X Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35 (6): 1397- 1409 doi: 10.1109/TPAMI.2012.213

[13]	张登银, 鞠铭烨, 王雪梅一种基于暗通道先验的快速图像去雾算法[J]. 电子学报, 2015, 43 (7): 1437- 1443 ZHANG Dengyin, JU Mingye, WANG Xuemei A fast image daze removal algorithm using dark channel prior[J]. Acta Electronica Sinica, 2015, 43 (7): 1437- 1443 doi: 10.3969/j.issn.0372-2112.2015.07.029

[14]	JU M, DING C, GUO Y J, et al IDGCP: image dehazing based on gamma correction prior[J]. IEEE Transactions on Image Processing, 2019, 29: 3104- 3118

[15]	ZHANG J, TAO D FAMED-net: a fast and accurate multi-scale end-to-end dehazing network[J]. IEEE Transactions on Image Processing, 2020, 29: 72- 84 doi: 10.1109/TIP.2019.2922837

[16]	REN W, MA L, ZHANG J, et al. Gated fusion network for single image dehazing [C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 3253–3261.

[17]	LIU X, MA Y, SHI Z, et al. GridDehazeNet: attention-based multi-scale network for image dehazing [C]// IEEE/CVF International Conference on Computer Vision. Seoul: IEEE, 2019: 7313–7322.

[18]	DAS S D, DUTTA S. Fast deep multi-patch hierarchical network for nonhomogeneous image dehazing [C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Seattle: IEEE, 2020: 1994–2001.

[19]	BALLA P K, KUMAR A, PANDEY R A 4-channelled hazy image input generation and deep learning-based single image dehazing[J]. Journal of Visual Communication and Image Representation, 2024, 100: 104099 doi: 10.1016/j.jvcir.2024.104099

[20]	QIN X, WANG Z, BAI Y, et al FFA-net: feature fusion attention network for single image dehazing[J]. AAAI Conference on Artificial Intelligence, 2020, 34 (7): 11908- 11915 doi: 10.1609/aaai.v34i07.6865

[21]	SHAO Y, LI L, REN W, et al. Domain adaptation for image dehazing [C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 2805–2814.

[22]	SONG Y, HE Z, QIAN H, et al Vision transformers for single image dehazing[J]. IEEE Transactions on Image Processing, 2023, 32: 1927- 1941 doi: 10.1109/TIP.2023.3256763

[23]	YU Y, ZHANG Y, CHENG Z, et al MCA: multidimensional collaborative attention in deep convolutional neural networks for image recognition[J]. Engineering Applications of Artificial Intelligence, 2023, 126: 107079 doi: 10.1016/j.engappai.2023.107079

[24]	ZHANG Y, TIAN Y, KONG Y, et al. Residual dense network for image super-resolution [C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 2472–2481.

[25]	YAN Q, GONG D, SHI Q, et al. Attention-guided network for ghost-free high dynamic range imaging [C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 1751–1760.

[26]	杨爱萍, 刘瑾, 邢金娜, 等基于内容特征和风格特征融合的单幅图像去雾网络[J]. 自动化学报, 2023, 49 (4): 769- 777 YANG Aiping, LIU Jin, XING Jinna, et al Content feature and style feature fusion network for single image dehazing[J]. Acta Automatica Sinica, 2023, 49 (4): 769- 777

[27]	WANG Z, BOVIK A C, SHEIKH H R, et al Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13 (4): 600- 612 doi: 10.1109/TIP.2003.819861

[28]	HE T, ZHANG Z, ZHANG H, et al. Bag of tricks for image classification with convolutional neural networks [C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 558–567.

[29]	KINGMA D P, BA J, HAMMAD M M. Adam: a method for stochastic optimization [EB/OL]. (2014−12−22)[2024−02−03]. https://arxiv.org/abs/1412.6980v9.

[30]	LI B, REN W, FU D, et al Benchmarking single image dehazing and beyond[J]. IEEE Transactions on Image Processing, 2018, 1: 492- 505

[31]	MANNOS J, SAKRISON D The effects of a visual fidelity criterion of the encoding of images[J]. IEEE Transactions on Information Theory, 1974, 20 (4): 525- 536 doi: 10.1109/TIT.1974.1055250

[32]	ZHANG Y, DING L, SHARMA G. HazeRD: an outdoor scene dataset and benchmark for single image dehazing [C]// IEEE International Conference on Image Processing. Beijing: IEEE, 2017: 3205–3209.

[33]	CHEN D, HE M, FAN Q, et al. Gated context aggregation network for image dehazing and deraining [C]// IEEE Winter Conference on Applications of Computer Vision. Waikoloa Village: IEEE, 2019: 1375–1383.

[34]	ZHENG Z, REN W, CAO X, et al. Ultra-high-definition image dehazing via multi-guided bilateral learning [C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 16180–16189.

[35]	BAI H, PAN J, XIANG X, et al Self-guided image dehazing using progressive feature fusion[J]. IEEE Transactions on Image Processing, 2022, 31: 1217- 1229 doi: 10.1109/TIP.2022.3140609

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