Semi-supervised Image Dehazing Algorithm Based on Multi-prior Constraint and Consistency Regularization

SU Yanzhao; HE Chuan; CUI Zhigao; JIANG Ke; CAI Yanping; LI Aihua

doi:10.11999/JEIT220381

Volume 44 Issue 10

Oct. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(10): 3409-3418

SU Yanzhao, HE Chuan, CUI Zhigao, JIANG Ke, CAI Yanping, LI Aihua. Semi-supervised Image Dehazing Algorithm Based on Multi-prior Constraint and Consistency Regularization[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3409-3418. doi: 10.11999/JEIT220381

Citation:

SU Yanzhao, HE Chuan, CUI Zhigao, JIANG Ke, CAI Yanping, LI Aihua. Semi-supervised Image Dehazing Algorithm Based on Multi-prior Constraint and Consistency Regularization[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3409-3418. doi: 10.11999/JEIT220381

Citation:

SU Yanzhao, HE Chuan, CUI Zhigao, JIANG Ke, CAI Yanping, LI Aihua. Semi-supervised Image Dehazing Algorithm Based on Multi-prior Constraint and Consistency Regularization[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3409-3418. doi: 10.11999/JEIT220381

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Semi-supervised Image Dehazing Algorithm Based on Multi-prior Constraint and Consistency Regularization

doi: 10.11999/JEIT220381

College of War Support, Rocket Force University of Engineering, Xi’an 710025, China

Funds: The National Natural Science Foundation of China (61773389), The Postdoctoral Science Foundation of China (2019M663635), The Shaanxi Young Science and Technology Star (2021KJXX-22), The Natural Science Foundation of Shaanxi Province (2020JQ-2)

Received Date: 2022-04-01
Rev Recd Date: 2022-08-25

Available Online: 2022-09-14

Publish Date: 2022-10-19

Abstract

Abstract

Previous dehazing models trained on synthetic hazy images can not generalize well on real hazy scenes and improve the performance of high-level vision tasks significantly. To resolve this issue, a semi-supervised image dehazing based on multi-priors constrain and output consistency regularization is proposed. The algorithm adopts the encoder and decoder network to train on the synthetic and real hazy images by sharing the parameters. Multi prior-based dehazed images are adopted as pseudo labels to constrain the real scene hazy images. Furthermore, to reduce the divergence of different prior-based methods, the dehazing results of the random mix-up real hazy images are regularized to be consistent with the corresponding mix-up of the prior-based dehazed images. Finally, the experiment results demonstrate the performance of the proposed algorithm compared with the state-of-the-art methods.
- Image dehazing,
- Semi-supervised learning,
- Multi-priors,
- Consistency regulation

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References(32)

References

[1]	李红云, 施云, 高银. 基于显著性权重的多曝光融合的单幅雾天图像复原算法[J]. 电子与信息学报, 2022, 44(1): 261–270. doi: 10.11999/JEIT200931 LI Hongyun, SHI Yun, and GAO Yin. Single image dehazing via saliency weighted multi-exposure fusion[J]. Journal of Electronics &Information Technology, 2022, 44(1): 261–270. doi: 10.11999/JEIT200931
[2]	YANG Wenhan, YUAN Ye, and REN Wenqi, et al. Advancing image understanding in poor visibility environments: A collective benchmark study[J]. IEEE Transactions on Image Processing, 2020, 29: 5737–5752. doi: 10.1109/TIP.2020.2981922
[3]	NAYAR S K and NARASIMHAN S G. Vision in bad weather[C]. Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, 1999: 820–827.
[4]	HE Kaiming, SUN Jian, and TANG Xiaoou. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12): 2341–2353. doi: 10.1109/TPAMI.2010.168
[5]	ZHU Qingsong, MAI Jiaming, and SHAO Ling. 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
[6]	FATTAL R. Dehazing using color-lines[J]. ACM Transactions on Graphics, 2014, 34(1): 13. doi: 10.1145/2651362
[7]	REN Wenqi, PAN Jinshan, ZHANG Hua, et al. Single image dehazing via multi-scale convolutional neural networks with holistic edges[J]. International Journal of Computer Vision, 2020, 128(1): 240–259. doi: 10.1007/s11263-019-01235-8
[8]	CAI Bolun, XU Xiangmin, JIA Kui, et al. DehazeNet: An end-to-end system for single image haze removal[J]. IEEE Transactions on Image Processing, 2016, 25(11): 5187–5198. doi: 10.1109/TIP.2016.2598681
[9]	ZHANG He and PATEL V M. Densely connected pyramid dehazing network[C]. The 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 3194–3203.
[10]	LIU Xiaohong, MA Yongrui, SHI Zhihao, et al. GridDehazeNet: Attention-based multi-scale network for image dehazing[C]. IEEE/CVF International Conference on Computer Vision, Seoul, Korea (South), 2019: 7313–7322.
[11]	QIN Xu, WANG Zhilin, BAI Yuanchao, et al. FFA-Net: Feature fusion attention network for single image dehazing[C]. 34th AAAI Conference on Artificial Intelligence, New York, USA, 2020: 11908–11915.
[12]	DONG Hang, PAN Jinshan, XIANG Lei, et al. Multi-scale boosted dehazing network with dense feature fusion[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, 2020: 2154–2164.
[13]	SU Yanzhao, CUI Zhigao, HE Chuan, et al. Prior guided conditional generative adversarial network for single image dehazing[J]. Neurocomputing, 2021, 423: 620–638. doi: 10.1016/j.neucom.2020.10.061
[14]	LI Lerenhan, DONG Yunlong, REN Wenqi, et al. Semi-supervised image dehazing[J]. IEEE Transactions on Image Processing, 2020, 29: 2766–2779. doi: 10.1109/TIP.2019.2952690
[15]	SHAO Yuanjie, LI Lerenhan, REN Wenqi, et al. Domain adaptation for image dehazing[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, 2020: 2805–2814.
[16]	CHEN Zeyuan, WANG Yangchao, YANG Yang, et al. PSD: Principled synthetic-to-real dehazing guided by physical priors[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2021: 7176–7185.
[17]	张世辉, 路佳琪, 宋丹丹, 等. 基于多尺度特征结合细节恢复的单幅图像去雾方法[J]. 电子与信息学报, 待发表. ZHANG Shihui, LU Jiaqi, SONG Dandan, et al. Single image dehazing method based on multi-scale features combined with detail recovery[J]. Journal of Electronics & Information Technology. To be published,
[18]	WANG Nian, CUI Zhigao, SU Yanzhao, et al. Multiscale supervision-guided context aggregation network for single image dehazing[J]. IEEE Signal Processing Letters, 2022, 29: 70–74. doi: 10.1109/LSP.2021.3125272
[19]	MEI Kangfu, JIANG Aiwen, LI Juncheng, et al. Progressive feature fusion network for realistic image dehazing[C]. 14th Asian Conference on Computer Vision, Perth, Australia, 2019: 203–215. DOI: 10.1007/978-3-030-20887-5_13.
[20]	ZHAO Hang, GALLO O, FROSIO I, et al. Loss functions for image restoration with neural networks[J]. IEEE Transactions on Computational Imaging, 2017, 3(1): 47–57. doi: 10.1109/TCI.2016.2644865
[21]	卢辉斌, 赵燕芳, 赵永杰, 等. 基于亮通道和暗通道结合的图像去雾[J]. 光学学报, 2018, 38(11): 1115004. doi: 10.3788/AOS201838.111500 LU Huibin, ZHAO Yanfang, ZHAO Yongjie, et al. Image defogging based on combination of image bright and dark channels[J]. Acta Optica Sinica, 2018, 38(11): 1115004. doi: 10.3788/AOS201838.111500
[22]	BERMAN D, TREIBITZ T, and AVIDAN S. Non-local image dehazing[C]. The 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 1674–1682.
[23]	GHIASI G, ZOPH B, CUBUK E D, et al. Multi-task self-training for learning general representations[C]. IEEE/CVF International Conference on Computer Vision, Montreal, Canada, 2021: 8836–8845.
[24]	YUN S, HAN D, CHUN S, et al. CutMix: Regularization strategy to train strong classifiers with localizable features[C]. IEEE/CVF International Conference on Computer Vision, Seoul, Korea (South), 2019: 6022–6031.
[25]	REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137–1149. doi: 10.1109/tpami.2016.2577031
[26]	LI Boyi, REN Wenqi, FU Dengpan, et al. Benchmarking single-image dehazing and beyond[J]. IEEE Transactions on Image Processing, 2019, 28(1): 492–505. doi: 10.1109/TIP.2018.2867951
[27]	ZHANG Yanfu, DING Li, and SHARMA G. HazeRD: An outdoor scene dataset and benchmark for single image dehazing[C]. 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China, 2017: 3205–3209.
[28]	ANCUTI C, ANCUTI C O, TIMOFTE R, et al. I-HAZE: A dehazing benchmark with real hazy and haze-free indoor images[C]. The 19th International Conference on Advanced Concepts for Intelligent Vision Systems, Poitiers, France, 2018: 620–631.
[29]	ANCUTI C O, ANCUTI C, TIMOFTE R, et al. O-HAZE: A dehazing benchmark with real hazy and haze-free outdoor images[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, USA, 2018: 867–875.
[30]	ZHAO Shiyu, ZHANG Lin, HUANG Shuaiyi, et al. Dehazing evaluation: Real-world benchmark datasets, criteria, and baselines[J]. IEEE Transactions on Image Processing, 2020, 29: 6947–6962. doi: 10.1109/TIP.2020.2995264
[31]	REDMON J and FARHADI A. YOLOv3: An incremental improvement[EB/OL].https://doi.org/10.48550/arXiv.1804.02767, 2018.
[32]	LI Chongyi, GUO Chunle, REN Wenqi, et al. An underwater image enhancement benchmark dataset and beyond[J]. IEEE Transactions on Image Processing, 2020, 29: 4376–4389. doi: 10.1109/TIP.2019.2955241