Abstract
From traditional algorithms based on handcrafted prior to learning algorithms based on neural networks, the image dehazing technique has gone through great development. The handcrafted prior-based methods need to first estimate the transmission map and atmosphere light in the atmospheric scattering model separately, and then calculate the final haze-free image, which often leads to a gradual accumulation of errors. In contrast, in the end-to-end neural network-based methods, supervised learning with labels is a major element for the improvement of the dehazing effect. But in the physical situation, paired (hazy, haze-free) images are difficult to collect, which limits the application scope of supervised dehazing. To address this deficiency, we propose a Decoupling Network for image dehazing via Unsupervised Contrastive Learning mechanism which is widely used in self-supervised representation learning, named UCLD-Net. Specifically, we use the estimated transmission map and atmosphere light to design the structure of UCLD-Net and introduce prior knowledge to construct its loss function. It is demonstrated by the experiments that UCLD-Net achieves comparable results in the dehazing experiments on the benchmark RESIDE dataset, which sufficiently verifies its effectiveness.
Z. Liu, T. Hong—Equal contribution.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Narasimhan, S.G., Nayar, S.K.: Vision and the atmosphere. Int. J. Comput. Vis. 48(3), 233–254 (2002). https://doi.org/10.1023/A:1016328200723
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2010)
Meng, G., Wang, Y., Duan, J., Xiang, S., Pan, C.: Efficient image dehazing with boundary constraint and contextual regularization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 617–624 (2013)
Zhu, Q., Mai, J., Shao, L.: A fast single image haze removal algorithm using color attenuation prior. IEEE Trans. Image Process. 24(11), 3522–3533 (2015)
Cai, B., Xiangmin, X., Jia, K., Qing, C., Tao, D.: Dehazenet: An end-to-end system for single image haze removal. IEEE Trans. Image Process. 25(11), 5187–5198 (2016)
Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., Yang, M.-H.: Single image dehazing via multi-scale convolutional neural networks. In: European Conference on Computer Vision. Springer, pp. 154–169 (2016)
Li, B., Peng, X., Wang, Z., Xu, J., Feng, D.: Aod-net: All-in-one dehazing network. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4770–4778 (2017)
Chen, D., et al.: IEEE winter conference on applications of computer vision (WACV). IEEE 2019, 1375–1383 (2019)
Qin, X., Wang, Z., Bai, Y., Xie, X., Jia, H.: Ffa-net: Feature fusion attention network for single image dehazing. Proc. AAAI Conf. Artif. Intell. 34, 11908–11915 (2020)
Wu, H., et al.: Contrastive learning for compact single image dehazing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10551–10560 (2021)
Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
Engin, D., Gen, A., Ekenel, H.K.: Cycle-dehaze: Enhanced cyclegan for single image dehazing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 825–833 (2018)
Anvari, Z., Athitsos, V.: Dehaze-glcgan: unpaired single image de-hazing via adversarial training. arXiv preprint arXiv:2008.06632 (2020)
Wang, Z., Ji, S.: Smoothed dilated convolutions for improved dense prediction. Data Min. Knowl. Disc. 35(4), 1470–1496 (2021)
Chen, Z., Wang, Y., Yang, Y., Liu, D.: Psd: Principled synthetic-to-real dehazing guided by physical priors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7180–7189 (2021)
Yang, X., Xu, Z., Luo, J.: Towards perceptual image dehazing by physics-based disentanglement and adversarial training. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Zheng, Y., Jia, S., Zhang, S., Tao, M., Wang, L.: Dehaze-aggan: Unpaired remote sensing image dehazing using enhanced attention-guide generative adversarial networks. IEEE Trans. Geosci. Remote Sens. 60, 1–13 (2022)
Li, L., et al.: Semi-supervised image dehazing. IEEE Trans. Image Process. 29, 2766–2779 (2019)
Wang, C., et al.: Eaa-net: A novel edge assisted attention network for single image dehazing. Knowl.-Based Syst. 228, 107279 (2021)
Li, B., Gou, Y., Gu, S., Liu, J.Z., Zhou, J.T., Peng, X.: You only look yourself: Unsupervised and untrained single image dehazing neural network. Int. J. Comput. Vis. 129(5), 1754–1767 (2021)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)
Mao, X., Li, Q., Xie, H., Lau, R.Y.K., Wang, Z., Smolley, S.P.: Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2794–2802 (2017)
Li, B., et al.: Reside: A benchmark for single image dehazing, vol. 1. arXiv preprint arXiv:1712.04143 (2017)
Ren, W., et al.: Gated fusion network for single image dehazing. In” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3253–3261 (2018)
Qu, Y., Chen, Y., Huang, J., Xie, Y.: Enhanced pix2pix dehazing network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8160–8168 (2019)
Dudhane, A., Patil, P.W., Murala, S.: An end-to-end network for image de-hazing and beyond. IEEE Trans. Emerg. Top. Comput. Intell. 6(1), 159–170 (2022)
Acknowledgements
This work is supported by the Natural Science Foundation of China under grant 62071171 and the high-performance computing platform of Peking University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, Z., Hong, T., Ma, J. (2023). UCLD-Net: Decoupling Network via Unsupervised Contrastive Learning for Image Dehazing. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science(), vol 14090. Springer, Singapore. https://doi.org/10.1007/978-981-99-4761-4_18
Download citation
DOI: https://doi.org/10.1007/978-981-99-4761-4_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-4760-7
Online ISBN: 978-981-99-4761-4
eBook Packages: Computer ScienceComputer Science (R0)