SpringerLink
Log in

Encoder-decoder networks with guided transmission map for effective image dehazing

  • Research
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

A plain-architecture and effective image dehazing scheme, called Encoder-Decoder Network with Guided Transmission Map (EDN-GTM), is proposed in this paper. Nowadays, neural networks are often built based on complex architectures and modules, which inherently prevent them from being efficiently deployed on general mobile platforms that are not integrated with latest deep learning operators. Hence, from a practical point of view, plain-architecture networks would be more appropriate for implementation. To this end, we aim to develop non-sophisticated networks with effective dehazing performance. A vanilla U-Net is adopted as a starting baseline, then extensive analyses have been conducted to derive appropriate training settings and architectural features that can optimize dehazing effectiveness. As a result, several modifications are applied to the baseline such as plugging spatial pyramid pooling to the bottleneck and replacing ReLU activation with Swish activation. Moreover, we found that the transmission feature estimated by Dark Channel Prior (DCP) can be utilized as an additional prior for a generative network to recover appealing haze-free images. Experimental results on various benchmark datasets have shown that the proposed EDN-GTM scheme can achieve state-of-the-art dehazing results as compared to prevailing dehazing methods which are built upon complex architectures. In addition, the proposed EDN-GTM model can be combined with YOLOv4 to witness an improvement in object detection performance in hazy weather conditions. The code of this work is publicly available at https://github.com/tranleanh/edn-gtm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

Code, Data, and Materials Availability

The data sets used in this paper are publicly available data sets: I-HAZE [21], O-HAZE [22], Dense-HAZE [23], NH-HAZE [24], RESIDE (SOTS-Outdoor and HSTS) [56], WAYMO [60], and Foggy Driving [63]. The source code of this paper is available at: https://github.com/tranleanh/edn-gtm.

References

  1. Meng, G.,Wang, Y.,Duan, J., **ang, 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)

  2. 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)

    Article  MathSciNet  Google Scholar 

  3. 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)

    Google Scholar 

  4. Cai, B., Xu, 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)

    Article  MathSciNet  Google Scholar 

  5. 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)

  6. 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)

  7. Zhang, H., Sindagi, V., Patel, V. M.: Multi-scale single image dehazing using perceptual pyramid deep network. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 902–911 (2018)

  8. Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: Shufflenet v2: Practical guidelines for efficient cnn architecture design. In: Proceedings of the European conference on computer vision (ECCV), pp. 116–131 (2018)

  9. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In : Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7132–7141 (2018)

  10. Woo, S., Park, J., Lee, J.-Y., Kweon, I. S.: Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp. 3–19 (2018)

  11. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K. Q.: Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700–4708 (2017)

  12. Chiang, C.-M., Tseng, Y., Xu, Y.-S., Kuo, H.-K., Tsai, Y.-M., Chen, G.-Y., Tan, K.-S., Wang, W.-T., Lin, Y.-C., Tseng, S.-Y. R., et al.: Deploying image deblurring across mobile devices: a perspective of quality and latency. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 502–503 (2020)

  13. Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer, pp. 234–241 (2015)

  14. Wu, H., Qu, Y., Lin, S., Zhou, J., Qiao, R., Zhang, Z., **e, Y., Ma, L.: Contrastive learning for compact single image dehazing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10 551–10 560 (2021)

  15. Qin, X., Wang, Z., Bai, Y., **e, X., Jia, H.: Ffa-net: feature fusion attention network for single image dehazing. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 11 908–11 915 (2020)

  16. Tran, L.-A., Moon, S., Park, D.-C.: A novel encoder-decoder network with guided transmission map for single image dehazing. Procedia Computer Science 204, 682–689 (2022)

    Article  Google Scholar 

  17. Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 228–242 (2007)

    Article  Google Scholar 

  18. Ren, W., Pan, J., Zhang, H., Cao, X., Yang, M.-H.: Single image dehazing via multi-scale convolutional neural networks with holistic edges. Int. J. Comput. Vision 128(1), 240–259 (2020)

    Article  Google Scholar 

  19. Zhang,H., Patel, V. M.: Densely connected pyramid dehazing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3194–3203 (2018)

  20. Kupyn, O., Budzan, V., Mykhailych, M., Mishkin, D., Matas, J.: Deblurgan: Blind motion deblurring using conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8183–8192 (2018)

  21. Ancuti, C., Ancuti, C. O., Timofte, R., De Vleeschouwer, C.: I-haze: a dehazing benchmark with real hazy and haze-free indoor images. In: International Conference on Advanced Concepts for Intelligent Vision Systems. Springer, pp. 620–631 (2018)

  22. Ancuti, C. O., Ancuti, C., Timofte, R., De Vleeschouwer, C.: “O-haze: a dehazing benchmark with real hazy and haze-free outdoor images. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 754–762 (2018)

  23. Ancuti, C.O., Ancuti, C., Sbert, M., Timofte, R.: Dense-haze: A benchmark for image dehazing with dense-haze and haze-free images. In: IEEE international conference on image processing (ICIP). IEEE 2019, 1014–1018 (2019)

  24. Ancuti, C. O., Ancuti, C., Timofte, R.: Nh-haze: An image dehazing benchmark with non-homogeneous hazy and haze-free images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 444–445 (2020)

  25. Qu, Y., Chen, Y., Huang, J., **e, Y.: Enhanced pix2pix dehazing network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8160–8168 (2019)

  26. Dong, Y., Liu, Y., Zhang, H., Chen, S., Qiao, Y.: Fd-gan: Generative adversarial networks with fusion-discriminator for single image dehazing. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 10 729–10 736 (2020)

  27. Engin, D., Genç, A., Kemal Ekenel, H.: 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)

  28. 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)

  29. Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: European conference on computer vision. Springer, pp. 694–711 (2016)

  30. He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2012)

    Article  Google Scholar 

  31. Salazar-Colores, S., Jiménez, H.M., Ortiz-Echeverri, C.J., Flores, G.: Desmoking laparoscopy surgery images using an image-to-image translation guided by an embedded dark channel. IEEE Access 8, 208-898–208-909 (2020)

    Article  Google Scholar 

  32. He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)

    Article  Google Scholar 

  33. Ramachandran, P., Zoph, B., Le, Q. V.: Searching for activation functions. ar**v preprint ar**v:1710.05941, (2017)

  34. Zhang, H., Sindagi, V., Patel, V.M.: Joint transmission map estimation and dehazing using deep networks. IEEE Trans. Circuits Syst. Video Technol. 30(7), 1975–1986 (2019)

    Google Scholar 

  35. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. Advances in neural information processing systems. 27 (2014)

  36. Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks, In: International conference on machine learning.PMLR, pp. 214–223 (2017)

  37. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition, ar**v preprint ar**v:1409.1556, (2014)

  38. Wang, C.-Y., Liao, H.-Y. M., Wu, Y.-H., Chen, P.-Y., Hsieh, J.-W., Yeh, I.-H.: Cspnet: a new backbone that can enhance learning capability of CNN. In: of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp. 390–391 (2020)

  39. Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y. M.: Yolov4: Optimal speed and accuracy of object detection, ar**v preprint ar**v:2004.10934, (2020)

  40. Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation, In: Proceedings of the European conference on computer vision (ECCV), pp. 801–818 (2018)

  41. Goodfellow, I., Bengio, Y., Courville, A.: Deep learning. MIT press (2016)

    Google Scholar 

  42. Maas, A. L., Hannun, A. Y., Ng, A. Y., et al.: Rectifier nonlinearities improve neural network acoustic models. In : Proc. icml. Citeseer, p. 3 (2013)

  43. Berman, D., Avidan, S., et al.: Non-local image dehazing. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1674–1682 (2016)

  44. Bui, T.M., Kim, W.: Single image dehazing using color ellipsoid prior. IEEE Trans. Image Process. 27(2), 999–1009 (2017)

    Article  MathSciNet  Google Scholar 

  45. 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 (WACV). IEEE 2019, 1375–1383 (2019)

  46. Misra, D.: Mish: A self regularized non-monotonic neural activation function, ar**v preprint ar**v:1908.08681, vol. 4, p. 2, (2019)

  47. Ren, W., Ma, L., Zhang, J., Pan, J., Cao, X., Liu, W., Yang, M.-H.: Gated fusion network for single image dehazing. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3253–3261 (2018)

  48. Liu, X., Ma, Y., Shi, Z., Chen, J.: Griddehazenet: Attention-based multi-scale network for image dehazing. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7314–7323 (2019)

  49. Dong, H., Pan, J., **ang, L., Hu, Z., Zhang, X., Wang, F., Yang, M.-H.: Multi-scale boosted dehazing network with dense feature fusion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2157–2167 (2020)

  50. Ju, M., Ding, C., Ren, W., Yang, Y., Zhang, D., Guo, Y.J.: Ide: Image dehazing and exposure using an enhanced atmospheric scattering model. IEEE Trans. Image Process. 30, 2180–2192 (2021)

    Article  Google Scholar 

  51. Hong, M., **e, Y., Li , C., Qu, Y.: Distilling image dehazing with heterogeneous task imitation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3462–3471 (2020)

  52. Thanh, D. N., Hue, N. M., Prasath, V. S., et al.: Single image dehazing based on adaptive histogram equalization and linearization of gamma correction. In: 2019 25th Asia-Pacific Conference on Communications (APCC). IEEE, pp. 36–40 (2019)

  53. Golts, A., Freedman, D., Elad, M.: Unsupervised single image dehazing using dark channel prior loss. IEEE Trans. Image Process. 29, 2692–2701 (2019)

    Article  Google Scholar 

  54. 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. Vision 129, 1754–1767 (2021)

    Article  Google Scholar 

  55. Yang, Y., Wang, C., Liu, R., Zhang, L., Guo, X., Tao, D.: Self-augmented unpaired image dehazing via density and depth decomposition. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2037–2046 (2022)

  56. Li, B., Ren, W., Fu, D., Tao, D., Feng, D., Zeng, W., Wang, Z.: Benchmarking single-image dehazing and beyond. IEEE Trans. Image Process. 28(1), 492–505 (2018)

    Article  MathSciNet  Google Scholar 

  57. Mehra, A., Mandal, M., Narang, P., Chamola, V.: Reviewnet: a fast and resource optimized network for enabling safe autonomous driving in hazy weather conditions. IEEE Trans. Intell. Transp. Syst. 22(7), 4256–4266 (2020)

    Article  Google Scholar 

  58. Mehta, A., Sinha, H., Narang, P., Mandal, M.: Hidegan: a hyperspectral-guided image dehazing gan. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 212–213 (2020)

  59. Mehta, S., Rastegari, M.: Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer, ar**v preprint ar**v:2110.02178, (2021)

  60. Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., et al.: Scalability in perception for autonomous driving: Waymo open dataset. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2446–2454 (2020)

  61. Godard, C., Mac Aodha, O., Firman, M., Brostow, G. J.: Digging into self-supervised monocular depth estimation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3828–3838 (2019)

  62. Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: The Kitti dataset. The International Journal of Robotics Research 32(11), 1231–1237 (2013)

    Article  Google Scholar 

  63. Sakaridis, C., Dai, D., Van Gool, L.: Semantic foggy scene understanding with synthetic data. Int. J. Comput. Vision 126(9), 973–992 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics Engineering, Myongji University, Yongin, 17058, South Korea

    Le-Anh Tran & Dong-Chul Park

Authors
  1. Le-Anh Tran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dong-Chul Park
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the conceptualization and methodology of the study. Besides, experimental design and manuscript writing were performed by Le-Anh Tran, while manuscript review/editing and supervision were performed by Dong-Chul Park. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Dong-Chul Park.

Ethics declarations

Statements and Declarations

The authors declare that we have no relevant financial interests that could have appeared to influence the work reported in this paper.

Conflict of interest

The authors declare no Conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tran, LA., Park, DC. Encoder-decoder networks with guided transmission map for effective image dehazing. Vis Comput (2024). https://doi.org/10.1007/s00371-024-03330-5

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00371-024-03330-5

Keywords

Search

Navigation