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Multimedia Systems
Published in: Multimedia Systems 4/2023

12-04-2023 | Regular Paper

Cascaded deep residual learning network for single image dehazing

Authors: Yizhong Yang, Ce Hou, Haixia Huang, Zhang Zhang, Guangjun Xie

Published in: Multimedia Systems | Issue 4/2023

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Abstract

Convolutional neural networks (CNNs) have achieved significant success in the field of single image dehazing. However, most existing deep dehazing models are based on atmospheric scattering model, which have high accumulate errors. Thus, Cascaded Deep Residual Learning Network for Single Image Dehazing (CDRLN) with encoder-decoder structure is proposed, which can directly restore the clean image from hazy image. The proposed algorithm consists of a primary network which predicts a residual map based on the entire image, and a sub-network which restores the haze-free image based on the residual image and the original hazy image. The encoder part of CDRLN embeds a context feature extraction module to fuse information effectively. In addition, the two-stage cascaded strategy can avoid feature dilution and restore detailed information, which reduces the color distortion in the dehazing process and generates a more natural, more real and less artifacts dehazed image. Experimental results demonstrate that the CDRLN surpasses previous state-of-the-art single image dehazing methods by a large margin on the synthetic datasets as well as real-world hazy images, and the visual effect of dehazed image is better.
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Literature
1.
Li, B., Peng, X., Wang, Z., Xu, J., Feng, D.: End-to-end united video dehazing and detection. In: The Thirty-Second AAAI Conference on Artificial Intelligence, pp. 7016–7023 (2018)
2.
Nalla, B.T., Sharma, T., Verma, N.K., et al.: Image dehazing for object recognition using faster RCNN. In: 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 01–07 (2018)
3.
Cai, B., Xiangmin, Xu., 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)MathSciNetCrossRefMATH
4.
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, pp. 154–169 (2016)
5.
Li, B., Peng, X., Wang, Z., Xu, J., Feng, D.: Aod-net: All-in-one dehazing network. In: IEEE International Conference on Computer Vision, pp. 4770–4778 (2017)
6.
Yang, D., Sun, J.: Proximal dehaze-net: a prior learning-based deep network for single image dehazing. In: European Conference on Computer Vision, pp. 702–717 (2018)
7.
Tan, R.T.: Visibility in bad weather from a single image. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2008, pp. 1–8 (2008)
8.
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)CrossRef
9.
Berman, D., Treibitz, T., Avidan, S.: Non-local image dehazing, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1674–1682 (2016)
10.
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)MathSciNetCrossRefMATH
11.
Chen, D., He, M., Fan, Q., Liao, J., Zhang, L., Hou, D., Yuan, L., Hua, G.: Gated context aggregation network for image dehazing and deraining. In: IEEE Winter Conference on Applications of Computer Vision, pp. 1375–1383, (2019)
12.
Engin, D., Genç, A., Ekenel, H.K.: Cycle-dehaze: enhanced cyclegan for single image dehazing. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 825–833 (2018)
13.
Li, R., Pan, J., Li, Z., Tang, J.: Single image dehazing via conditional generative adversarial network. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 8202–8211 (2018)
14.
Li, Y., Miao, Q., Ouyang, W., Ma, Z., Fang, H., Dong, C., Quan, Y.: Lap-net: Levelaware progressive network for image dehazing. In: IEEE/CVF International Conference on Computer Vision, pp. 3276–3285 (2019)
15.
Zhang, J., Tao, D.: Famed-net: A fast and accurate multi-scale end-to-end dehazing network. IEEE TIP. 29, 72–84 (2020)MathSciNetMATH
16.
Zhang, H., Patel, V.M.: Densely connected pyramid dehazing network. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3194–3203 (2018)
17.
Yang, A., Wang, H., Ji, Z., Pang, Y., Shao, L.: Dual-path in dual-path network for single image dehazing. In: Proc. 28th Int. Joint Conf. Artif. Intell., Aug. 2019, pp. 4627–4634 (2019)
18.
Wenqi Ren, Lin Ma, Jiawei Zhang, Jinshan Pan, Xiaochun Cao, Wei Liu, and Ming-Hsuan Yang.: Gated fusion network for single image dehazing. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3253–3261 (2018)
19.
Deng, Z., Zhu, L., Hu, X., Fu, C.-W., Xu, X., Zhang, Q., Qin, J., Heng, P.-A.: Deep multi-model fusion for single-image dehazing. In: IEEE/CVF international conference on computer vision, pp. 2453–2462 (2019)
20.
Hong, M., Xie, Y., Li, C., Qu, Y.: Distilling image dehazing with heterogeneous task imitation. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3462–3471 (2020)
21.
Shao, Y., Li, L., Ren, W., Gao, C., Sang, N.: Domain adaptation for image dehazing. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2808–2817 (2020)
22.
Qu, Y., Chen, Y., Huang, J., Xie, Y.: Enhanced pix2pix dehazing network. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8160–8168 (2019)
23.
Liu, Y., Pan, J., Ren, J., Su, Z.: Learning deep priors for image dehazing. In: IEEE International Conference on Computer Vision, pp. 2492–2500 (2019)
24.
Kar, A., Dhara, S.K., Sen, D., Biswas, P.K.: Transmission map and atmospheric light guided iterative updater network for single image dehazing. arXiv preprint arXiv:​2008.​01701 (2020)
25.
Wang, C., Chen, R., Lu, Y., et al.: Recurrent context aggregation network for single image dehazing. IEEE Signal Process. Lett. 28, 419–423 (2021)CrossRef
26.
Zhang, K., Li, Y.: Single image dehazing via semi-supervised domain translation and architecture search. IEEE Signal Process. Lett. 28, 2127–2131 (2021)CrossRef
27.
28.
Sulami, M., et al.: Automatic recovery of the atmospheric light in hazy images. In: 2014 IEEE International Conference on Computational Photography (ICCP), pp. 1–11 (2014)
29.
30.
Chen, W.-T., Ding, J.-J., Kuo, S.-Y.: PMS-Net: Robust haze removal based on patch map for single images. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11681–11689 (2019)
31.
Liu, X., Suganuma, M., Sun, Z., Okatani, T.: Dual residual networks leveraging the potential of paired operations for image restoration. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 7007– 7016 (2019)
32.
J. Pan, J. et al.: Learning dual convolutional neural networks for low-level vision. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3070–3079 (2018)
33.
Dong, H., Pan, J., Xiang, L., et al.: Multi-scale boosted dehazing network with dense feature fusion. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2157–2167 (2020)
34.
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
35.
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards realtime object detection with region proposal networks. IEEE Trans. Pattern Analysis and Machine Intelligence. 39(6), 1137–1149 (2017)
36.
Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In European Conference on Computer Vision (ECCV), pp. 694–711. Springer, (2016)
37.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv: 1409.1556, (2014)
38.
Russakovsky, O., Deng, J., Hao, Su., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211–252 (2015)MathSciNetCrossRef
39.
Li, B., Ren, W., Dengpan, Fu., Tao, D., Feng, D., Zeng, W., Wang, Z.: Benchmarking single image dehazing and beyond. IEEE Trans. Image Process. 28(1), 492–505 (2019)MathSciNetCrossRefMATH
40.
Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from rgbd images. In: European Conference on Computer Vision (ECCV), pp. 746–760. Springer (2012)
41.
Scharstein, D., Szeliski, R.: High-accuracy stereo depth maps using structured light. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. I-195–202 (2003)
42.
Ancuti, C., et al.: I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images. In: International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 620–631 (2018)
43.
Ancuti, C.O., Ancuti, C., Timofte, R., & De Vleeschouwer, C.: O-haze: a dehazing benchmark with real hazy and haze-free outdoor images. In: IEEE Conference on computer vision and Pattern Recognition Workshops, pp. 754–762 (2018)
44.
Ancuti, C., Ancuti, C.O., Timofte, R.: Ntire 2018 challenge on image dehazing: Methods and results. In: IEEE Conference on computer vision and Pattern Recognition Workshops, pp. 891–901 (2018)
45.
Zhang, X., et al.: Single image Dehazing via dual-path recurrent network. IEEE Trans. Image Process. 30, 5211–5222 (2021)CrossRef
46.
Liu, X., Ma., Y., Shi, Z., Chen, J.: Griddehazenet: Attention-based multi-scale network for image dehazing. In IEEE International Conference on Computer Vision, pp. 7314–7323 (2019)
47.
Mei, K., Jiang, A., Li, J., Wang, M.: Progressive feature fusion network for realistic image dehazing. In: Asian Conference on Computer Vision, pp. 203–215 (2018)
48.
Dong, Y., et al.: FD-GAN: Generative adversarial networks with fusion-discriminator for single image dehazing. In: Proceedings of the AAAI Conference on Artificial Intelligence. 34(07), 10729–10736 (2020).
Metadata
Title
Cascaded deep residual learning network for single image dehazing
Authors
Yizhong Yang
Ce Hou
Haixia Huang
Zhang Zhang
Guangjun Xie
Publication date
12-04-2023
Publisher
Springer Berlin Heidelberg
Published in
Multimedia Systems / Issue 4/2023
Print ISSN: 0942-4962
Electronic ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-023-01087-w

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