research-article

Source-Free Domain Adaptation for Real-World Image Dehazing

Authors:

Feng ZhaoAuthors Info & Claims

MM '22: Proceedings of the 30th ACM International Conference on Multimedia

Pages 6645 - 6654

https://doi.org/10.1145/3503161.3548410

Published: 10 October 2022 Publication History

Abstract

Deep learning-based source dehazing methods trained on synthetic datasets have achieved remarkable performance but suffer from dramatic performance degradation on real hazy images due to domain shift. Although certain Domain Adaptation (DA) dehazing methods have been presented, they inevitably require access to the source dataset to reduce the gap between the source synthetic and target real domains. To address these issues, we present a novel Source-Free Unsupervised Domain Adaptation (SFUDA) image dehazing paradigm, in which only a well-trained source model and an unlabeled target real hazy dataset are available. Specifically, we devise the Domain Representation Normalization (DRN) module to make the representation of real hazy domain features match that of the synthetic domain to bridge the gaps. With our plug-and-play DRN module, unlabeled real hazy images can adapt existing well-trained source networks. Besides, the unsupervised losses are applied to guide the learning of the DRN module, which consists of frequency losses and physical prior losses. Frequency losses provide structure and style constraints, while the prior loss explores the inherent statistic property of haze-free images. Equipped with our DRN module and unsupervised loss, existing source dehazing models are able to dehaze unlabeled real hazy images. Extensive experiments on multiple baselines demonstrate the validity and superiority of our method visually and quantitatively.

References

[1]

Sk Miraj Ahmed, Dripta S Raychaudhuri, Sujoy Paul, Samet Oymak, and Amit K Roy-Chowdhury. 2021. Unsupervised multi-source domain adaptation without access to source data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10103--10112.

[2]

Cosmin Ancuti, Codruta O Ancuti, Radu Timofte, and Christophe De Vleeschouwer. 2018. I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images. In International Conference on Advanced Concepts for Intelligent Vision Systems. Springer, 620--631.

[3]

Dana Berman, Shai Avidan, et al. 2016. Non-local image dehazing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1674--1682.

[4]

Bolun Cai, Xiangmin Xu, Kui Jia, Chunmei Qing, and Dacheng Tao. 2016. Dehazenet: An end-to-end system for single image haze removal. IEEE Transactions on Image Processing, Vol. 25, 11 (2016), 5187--5198.

Digital Library

[5]

Chen Chen, Minh N Do, and Jue Wang. 2016. Robust image and video dehazing with visual artifact suppression via gradient residual minimization. In Proceedings of the European Conference on Computer Vision (ECCV). Springer, 576--591.

[6]

Zeyuan Chen, Yangchao Wang, Yang Yang, and Dong Liu. 2021. PSD: Principled synthetic-to-real dehazing guided by physical priors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7180--7189.

[7]

Boris Chidlovskii, Stephane Clinchant, and Gabriela Csurka. 2016. Domain adaptation in the absence of source domain data. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 451--460.

Digital Library

[8]

Qili Deng, Ziling Huang, Chung-Chi Tsai, and Chia-Wen Lin. 2020. Hardgan: A haze-aware representation distillation gan for single image dehazing. In Proceedings of the European Conference on Computer Vision (ECCV). Springer, 722--738.

Digital Library

[9]

Hang Dong, Jinshan Pan, Lei Xiang, Zhe Hu, Xinyi Zhang, Fei Wang, and Ming-Hsuan Yang. 2020. Multi-scale boosted dehazing network with dense feature fusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2157--2167.

[10]

Jiangxin Dong and Jinshan Pan. 2020. Physics-based feature dehazing networks. In Proceedings of the European Conference on Computer Vision (ECCV). Springer, 188--204.

Digital Library

[11]

Xinjie Fan, Qifei Wang, Junjie Ke, Feng Yang, Boqing Gong, and Mingyuan Zhou. 2021. Adversarially adaptive normalization for single domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 8208--8217.

[12]

Raanan Fattal. 2008. Single image dehazing. ACM Transactions on Graphics (TOG), Vol. 27, 3 (2008), 1--9.

Digital Library

[13]

Raanan Fattal. 2014. Dehazing using color-lines. ACM Transactions on Graphics (TOG), Vol. 34, 1 (2014), 1--14.

Digital Library

[14]

Kaiming He, Jian Sun, and Xiaoou Tang. 2010. Single image haze removal using dark channel prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, 12 (2010), 2341--2353.

[15]

Tong He, Zhi Zhang, Hang Zhang, Zhongyue Zhang, Junyuan Xie, and Mu Li. 2019. Bag of tricks for image classification with convolutional neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 558--567.

[16]

Ming Hong, Yuan Xie, Cuihua Li, and Yanyun Qu. 2020. Distilling image dehazing with heterogeneous task imitation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3462--3471.

[17]

Jiaxing Huang, Dayan Guan, Aoran Xiao, and Shijian Lu. 2021. Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data. Advances in Neural Information Processing Systems, Vol. 34 (2021).

[18]

Xun Huang and Serge Belongie. 2017. Arbitrary style transfer in real-time with adaptive instance normalization. In Proceedings of the IEEE International Conference on Computer Vision. 1501--1510.

[19]

Songhao Jia, Ding-Jie Chen, and Hwann-Tzong Chen. 2019. Instance-level meta normalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4865--4873.

[20]

Xin Jin, Cuiling Lan, Wenjun Zeng, Zhibo Chen, and Li Zhang. 2020. Style normalization and restitution for generalizable person re-identification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3143--3152.

[21]

Boyun Li, Yuanbiao Gou, Shuhang Gu, Jerry Zitao Liu, Joey Tianyi Zhou, and Xi Peng. 2021. You only look yourself: Unsupervised and untrained single image dehazing neural network. International Journal of Computer Vision, Vol. 129, 5 (2021), 1754--1767.

[22]

Boyun Li, Yuanbiao Gou, Jerry Zitao Liu, Hongyuan Zhu, Joey Tianyi Zhou, and Xi Peng. 2020a. Zero-shot image dehazing. IEEE Transactions on Image Processing, Vol. 29 (2020), 8457--8466.

[23]

Boyi Li, Xiulian Peng, Zhangyang Wang, Jizheng Xu, and Dan Feng. 2017. Aod-net: All-in-one dehazing network. In Proceedings of the IEEE International Conference on Computer Vision. 4770--4778.

[24]

Boyi Li, Wenqi Ren, Dengpan Fu, Dacheng Tao, Dan Feng, Wenjun Zeng, and Zhangyang Wang. 2018b. Benchmarking single-image dehazing and beyond. IEEE Transactions on Image Processing, Vol. 28, 1 (2018), 492--505.

Digital Library

[25]

Lerenhan Li, Yunlong Dong, Wenqi Ren, Jinshan Pan, Changxin Gao, Nong Sang, and Ming-Hsuan Yang. 2019b. Semi-supervised image dehazing. IEEE Transactions on Image Processing, Vol. 29 (2019), 2766--2779.

[26]

Rui Li, Qianfen Jiao, Wenming Cao, Hau-San Wong, and Si Wu. 2020b. Model adaptation: Unsupervised domain adaptation without source data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 9641--9650.

[27]

Runde Li, Jinshan Pan, Zechao Li, and Jinhui Tang. 2018a. Single image dehazing via conditional generative adversarial network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 8202--8211.

[28]

Siyuan Li, Iago Breno Araujo, Wenqi Ren, Zhangyang Wang, Eric K Tokuda, Roberto Hirata Junior, Roberto Cesar-Junior, Jiawan Zhang, Xiaojie Guo, and Xiaochun Cao. 2019a. Single image deraining: A comprehensive benchmark analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3838--3847.

[29]

Risheng Liu, Xin Fan, Minjun Hou, Zhiying Jiang, Zhongxuan Luo, and Lei Zhang. 2018. Learning aggregated transmission propagation networks for haze removal and beyond. IEEE Transactions on Neural Networks and Learning Systems, Vol. 30, 10 (2018), 2973--2986.

[30]

Xiaohong Liu, Yongrui Ma, Zhihao Shi, and Jun Chen. 2019a. Griddehazenet: Attention-based multi-scale network for image dehazing. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 7314--7323.

[31]

Yang Liu, Jinshan Pan, Jimmy Ren, and Zhixun Su. 2019b. Learning deep priors for image dehazing. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 2492--2500.

[32]

Yuang Liu, Wei Zhang, and Jun Wang. 2021a. Source-free domain adaptation for semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1215--1224.

[33]

Ye Liu, Lei Zhu, Shunda Pei, Huazhu Fu, Jing Qin, Qing Zhang, Liang Wan, and Wei Feng. 2021b. From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data. In Proceedings of the 29th ACM International Conference on Multimedia. 50--58.

Digital Library

[34]

Earl J McCartney. 1976. Optics of the atmosphere: scattering by molecules and particles. New York (1976).

[35]

Anish Mittal, Anush Krishna Moorthy, and Alan Conrad Bovik. 2012a. No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing, Vol. 21, 12 (2012), 4695--4708.

Digital Library

[36]

Anish Mittal, Rajiv Soundararajan, and Alan C Bovik. 2012b. Making a completely blind" image quality analyzer. IEEE Signal Processing Letters, Vol. 20, 3 (2012), 209--212.

[37]

Alan V Oppenheim and Jae S Lim. 1981. The importance of phase in signals. Proc. IEEE, Vol. 69, 5 (1981), 529--541.

[38]

Xingang Pan, Ping Luo, Jianping Shi, and Xiaoou Tang. 2018. Two at once: Enhancing learning and generalization capacities via ibn-net. In Proceedings of the European Conference on Computer Vision (ECCV). 464--479.

Digital Library

[39]

Zhihong Pan, Baopu Li, Dongliang He, Mingde Yao, Wenhao Wu, Tianwei Lin, Xin Li, and Errui Ding. 2022. Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 17389--17398.

[40]

Stephen M Pizer, E Philip Amburn, John D Austin, Robert Cromartie, Ari Geselowitz, Trey Greer, Bart ter Haar Romeny, John B Zimmerman, and Karel Zuiderveld. 1987. Adaptive histogram equalization and its variations. Computer Vision, Graphics, and Image Processing, Vol. 39, 3 (1987), 355--368.

Digital Library

[41]

Xu Qin, Zhilin Wang, Yuanchao Bai, Xiaodong Xie, and Huizhu Jia. 2020. FFA-Net: Feature fusion attention network for single image dehazing. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 11908--11915.

[42]

Yanyun Qu, Yizi Chen, Jingying Huang, and Yuan Xie. 2019. Enhanced pix2pix dehazing network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 8160--8168.

[43]

Wenqi Ren, Si Liu, Hua Zhang, Jinshan Pan, Xiaochun Cao, and Ming-Hsuan Yang. 2016. Single image dehazing via multi-scale convolutional neural networks. In Proceedings of the European Conference on Computer Vision (ECCV). Springer, 154--169.

[44]

Wenqi Ren, Lin Ma, Jiawei Zhang, Jinshan Pan, Xiaochun Cao, Wei Liu, and Ming-Hsuan Yang. 2018. Gated fusion network for single image dehazing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3253--3261.

[45]

Ali M Reza. 2004. Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. Journal of Signal Processing Systems for Signal Image and Video Technology, Vol. 38, 1 (2004), 35--44.

Digital Library

[46]

Yuanjie Shao, Lerenhan Li, Wenqi Ren, Changxin Gao, and Nong Sang. 2020. Domain adaptation for image dehazing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2808--2817.

[47]

Pranjay Shyam, Kuk-Jin Yoon, and Kyung-Soo Kim. 2021. Towards domain invariant single image dehazing. arXiv preprint arXiv:2101.10449 (2021).

[48]

Nikolay Skarbnik, Yehoshua Y Zeevi, and Chen Sagiv. 2009. The importance of phase in image processing. Technion-Israel Institute of Technology, Faculty of Electrical Engineering.

[49]

Rosaura G VidalMata, Sreya Banerjee, Brandon RichardWebster, Michael Albright, Pedro Davalos, Scott McCloskey, Ben Miller, Asong Tambo, Sushobhan Ghosh, Sudarshan Nagesh, et al. 2020. Bridging the gap between computational photography and visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 43, 12 (2020), 4272--4290.

Digital Library

[50]

Chao Wang, Hao-Zhen Shen, Fan Fan, Ming-Wen Shao, Chuan-Sheng Yang, Jian-Cheng Luo, and Liang-Jian Deng. 2021b. EAA-Net: A novel edge assisted attention network for single image dehazing. Knowledge-Based Systems, Vol. 228 (2021), 107279.

Digital Library

[51]

Menglu Wang, Xueyang Fu, Zepei Sun, and Zheng-Jun Zha. 2021a. JPEG artifacts removal via compression quality ranker-guided networks. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence. 566--572.

Digital Library

[52]

Haiyan Wu, Yanyun Qu, Shaohui Lin, Jian Zhou, Ruizhi Qiao, Zhizhong Zhang, Yuan Xie, and Lizhuang Ma. 2021. Contrastive Learning for Compact Single Image Dehazing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10551--10560.

[53]

Wenhan Yang, Ye Yuan, Wenqi Ren, Jiaying Liu, Walter J Scheirer, Zhangyang Wang, Taiheng Zhang, Qiaoyong Zhong, Di Xie, Shiliang Pu, et al. 2020. Advancing image understanding in poor visibility environments: A collective benchmark study. IEEE Transactions on Image Processing, Vol. 29 (2020), 5737--5752.

[54]

Mingde Yao, Zhiwei Xiong, Lizhi Wang, Dong Liu, and Xuejin Chen. 2019. Spectral-depth imaging with deep learning based reconstruction. Optics Express, Vol. 27, 26 (2019), 38312--38325.

[55]

Tian Ye, Mingchao Jiang, Yunchen Zhang, Liang Chen, Erkang Chen, Pen Chen, and Zhiyong Lu. 2021. Perceiving and Modeling Density is All You Need for Image Dehazing. arXiv preprint arXiv:2111.09733 (2021).

[56]

Tian Ye, Yun Liu, Yunchen Zhang, Sixiang Chen, and Erkang Chen. 2022. Mutual Learning for Domain Adaptation: Self-distillation Image Dehazing Network with Sample-cycle. arXiv preprint arXiv:2203.09430 (2022).

[57]

He Zhang and Vishal M Patel. 2018. Densely connected pyramid dehazing network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3194--3203.

[58]

Jing Zhang, Yang Cao, Shuai Fang, Yu Kang, and Chang Wen Chen. 2017. Fast haze removal for nighttime image using maximum reflectance prior. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 7418--7426.

[59]

Zhuoran Zheng, Wenqi Ren, Xiaochun Cao, Xiaobin Hu, Tao Wang, Fenglong Song, and Xiuyi Jia. 2021. Ultra-high-definition image dehazing via multi-guided bilateral learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 16180--16189.

[60]

Qingsong Zhu, Jiaming Mai, and Ling Shao. 2014. Single Image Dehazing Using Color Attenuation Prior. In British Machine Vision Conference(BMVC). Citeseer.

[61]

Qingsong Zhu, Jiaming Mai, and Ling Shao. 2015. A fast single image haze removal algorithm using color attenuation prior. IEEE Transactions on Image Processing, Vol. 24, 11 (2015), 3522--3533.io

Digital Library

Cited By

Feng YMa LMeng XZhou FLiu RSu Z(2024)Advancing Real-World Image Dehazing: Perspective, Modules, and TrainingIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.341673146:12(9303-9320)Online publication date: Dec-2024
https://doi.org/10.1109/TPAMI.2024.3416731
Li JYu ZDu ZZhu LShen H(2024)A Comprehensive Survey on Source-Free Domain AdaptationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.337097846:8(5743-5762)Online publication date: Aug-2024
https://doi.org/10.1109/TPAMI.2024.3370978
Yang ZHuang JZhou MZheng NZhao F(2024)IRVR: A General Image Restoration Framework for Visual RecognitionIEEE Transactions on Multimedia10.1109/TMM.2024.335896226(7012-7026)Online publication date: 26-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3358962
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Index Terms

Source-Free Domain Adaptation for Real-World Image Dehazing
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene understanding

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cover image ACM Conferences

MM '22: Proceedings of the 30th ACM International Conference on Multimedia

October 2022

7537 pages

ISBN:9781450392037

DOI:10.1145/3503161

General Chairs:
João Magalhães
NOVA University of Lisbon, Portugal
,
Alberto del Bimbo
University of Florence, Italy
,
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Nicu Sebe
University of Trento, Italy
,
Program Chairs:
Xavier Alameda-Pineda
Inria, Grenoble, France
,
Qin Jin
Renmin University of China, China
,
Vincent Oria
New Jersey Institute of Technology, USA
,
Laura Toni
University College London, UK

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Published: 10 October 2022

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MM '22: The 30th ACM International Conference on Multimedia

October 10 - 14, 2022

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Cited By

Feng YMa LMeng XZhou FLiu RSu Z(2024)Advancing Real-World Image Dehazing: Perspective, Modules, and TrainingIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.341673146:12(9303-9320)Online publication date: Dec-2024
https://doi.org/10.1109/TPAMI.2024.3416731
Li JYu ZDu ZZhu LShen H(2024)A Comprehensive Survey on Source-Free Domain AdaptationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.337097846:8(5743-5762)Online publication date: Aug-2024
https://doi.org/10.1109/TPAMI.2024.3370978
Yang ZHuang JZhou MZheng NZhao F(2024)IRVR: A General Image Restoration Framework for Visual RecognitionIEEE Transactions on Multimedia10.1109/TMM.2024.335896226(7012-7026)Online publication date: 26-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3358962
Li HLin ZQiu ZLi ZNiu KGuo NFu HHu YLiu J(2024)Enhancing and Adapting in the Clinic: Source-Free Unsupervised Domain Adaptation for Medical Image EnhancementIEEE Transactions on Medical Imaging10.1109/TMI.2023.333565143:4(1323-1336)Online publication date: Apr-2024
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Feng YSu ZMa LLi XLiu RZhou F(2024)Bridging the Gap Between Haze Scenarios: A Unified Image Dehazing ModelIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.341467734:11(11070-11085)Online publication date: Nov-2024
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He TXia ZChen JLi HChan S(2024)Target-agnostic Source-free Domain Adaptation for Regression Tasks2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00121(1464-1477)Online publication date: 13-May-2024
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Guichemerre ABelharbi SMayet TMurtaza SShamsolmoali PMcCaffrey LGranger E(2024)Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00008(33-43)Online publication date: 17-Jun-2024
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Fan JWeng JWang KYang YQian JLi JYang J(2024)Driving-Video Dehazing with Non-Aligned Regularization for Safety Assistance2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02467(26109-26119)Online publication date: 16-Jun-2024
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Hu SSun HLi BWei DLi WLu J(2024)Fast Adaptation for Human Pose Estimation via Meta-Optimization2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00176(1792-1801)Online publication date: 16-Jun-2024
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