GAN-based dehazing network with knowledge transferring | Multimedia Tools and Applications Skip to main content
Springer Nature Link
Log in

GAN-based dehazing network with knowledge transferring

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Capturing images under the condition of haze often shows low contrast and fades the color. Restoring the haze-free image from a single image is a challenging task due to the ill-pose of the problem and high degradation. To solve this problem, we propose a GAN (Generative Adversarial Network) Prior Guided Dehazing Network (GPGDN). While the prior dehazing methods often trained the model with adversarial loss to obtain a photorealistic dehazed result, The proposed method explores to transfer of the rich and diverse priors learned from large clean images to dehazing problem. The GPGDN consists of an Encoder and a GAN-based decoder. The Encoder is designed to generate the latent code and noise input, which are fed to GAN-based decoder and generate the final dehazed result. Due to the high degradation of dense haze areas, it is hard to restore high-quality results for these areas. The proposed method can transfer knowledge from the haze-free images into dehazed results and restore high-quality results. The experiment on simulated outdoor hazy images demonstrates that the proposed method outperforms other methods with a significant gap of 3.40dB. Hazy images dehazing by GPGDN show a clear improvement compared to prior methods.

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
¥17,985 /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 (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data Availibility

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE Conference on computer vision and pattern recognition, pp 770–778

  2. Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: IEEE Conference on computer vision and pattern recognition, pp 2881–2890

  3. Narasimhan SG, Nayar SK (2002) Vision and the atmosphere. Int J Comput Vision 48(3):233–254

    Article  Google Scholar 

  4. Shao Y, Li L, Ren W, Gao C, Sang N (2020) Domain adaptation for image dehazing. In: IEEE Conference on computer vision and pattern recognition

  5. Pan J, Dong J, Liu Y, Zhang J, Ren J, Tang J, Tai Y, Yang M-H (2020) Physics-based generative adversarial models for image restoration and beyond. IEEE Trans Pattern Anal Mach Intell PP:1–1. https://doi.org/10.1109/TPAMI.2020.2969348

  6. Schechner YY, Narasimhan SG, Nayar SK (2001) Instant dehazing of images using polarization. In: IEEE Conference on computer vision and pattern recognition

  7. Kopf J, Neubert B, Chen B, Cohen M, Cohen-Or D, Deussen O, Uyttendaele M, Lischinski D (2008) Deep photo: Model-based photograph enhancement and viewing. ACM Trans Graph 27:116

    Article  Google Scholar 

  8. He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341–2353

    Article  Google Scholar 

  9. Berman D, Avidan S et al (2016) Non-local image dehazing. In: IEEE Conference on computer vision and pattern recognition

  10. Fattal R (2014) Dehazing using color-lines. ACM Trans Graph 34(1):13

    Google Scholar 

  11. Wang W, Yuan X, Wu X, Liu Y (2017) Dehazing for images with large sky region. Neurocomputing 238:365–376

    Article  Google Scholar 

  12. Meng G, Wang Y, Duan J, Xiang S, Pan C (2013) Efficient image dehazing with boundary constraint and contextual regularization. In: IEEE International conference on computer vision

  13. Nyo MT, Mebarek-Oudina F, Hlaing SS, Khan NA (2022) Otsu’s thresholding technique for MRI image brain tumor segmentation. Multimed Tools and Appl 81(30):43837–43849. https://doi.org/10.1007/s11042-022-13215-1

    Article  Google Scholar 

  14. Ahmad S, Mehfuz S, Mebarek-Oudina F, Beg J (2022) RSM analysis based cloud access security broker: a systematic literature review. Clust Comput 25(5):3733–3763. https://doi.org/10.1007/s10586-022-03598-z

    Article  Google Scholar 

  15. Ren W, Liu S, Zhang H, Pan J, Cao X, Yang M-H (2016) Single image dehazing via multi-scale convolutional neural networks. In: European conference on computer vision

  16. Cai B, Xu X, Jia K, Qing C, Tao D (2016) Dehazenet: An end-to-end system for single image haze removal. IEEE Trans Image Process 25(11):5187–5198

    Article  MathSciNet  Google Scholar 

  17. Zhang H, Patel VM (2018) Densely connected pyramid dehazing network. In: IEEE Conference on computer vision and pattern recognition

  18. Li B, Peng X, Wang Z, Xu J, Feng D (2017) An all-in-one network for dehazing and beyond. In: IEEE International conference on computer vision

  19. Zheng Z, Ren W, Cao X, Hu X, Wang T, Song F, Jia X (2021) Ultra-high-definition image dehazing via multi-guided bilateral learning. In: IEEE Conference on computer vision and pattern recognition, pp 16185–16194

  20. Ren W, Pan J, Zhang H, Cao X, Yang M-H (2020) Single Image Dehazing via Multi-scale Convolutional Neural Networks with Holistic Edges. Int J Comput Vision 128(1):240–259. https://doi.org/10.1007/s11263-019-01235-8

    Article  Google Scholar 

  21. Li Y, Chen X, Kong C, Dai L, Huang Y (2022) A deep hourglass-structured fusion model for efficient single image dehazing. Multimed Tools Appl 81(24):35247–35260. https://doi.org/10.1007/s11042-022-12312-5

    Article  Google Scholar 

  22. Zhang J, He F, Duan Y, Yang S (2023) Aidednet anti-interference and detail enhancement dehazing. Front Comput Sci 17

  23. Liu Y, Yan Z, Tan J, Li Y (2023) Multi-purpose oriented single nighttime image haze removal based on unified variational retinex model. IEEE Trans Circ Syst Vid Technol 33(4):1643–1657. https://doi.org/10.1109/TCSVT.2022.3214430

    Article  Google Scholar 

  24. Ren W, Ma L, Zhang J, Pan J, Cao X, Liu W, Yang M-H (2018) Gated fusion network for single image dehazing. In: IEEE Conference on computer vision and pattern recognition

  25. Qu Y, Chen Y, Huang J, Xie Y (2019) Enhanced pix2pix dehazing network. In: IEEE Conference on computer vision and pattern recognition

  26. Ren W, Zhang J, Xu X, Ma L, Cao X, Meng G, Liu W (2019) Deep video dehazing with semantic segmentation. IEEE Trans Image Process 28(4):1895–1908. https://doi.org/10.1109/TIP.2018.2876178

    Article  MathSciNet  Google Scholar 

  27. Karras T, Laine S, Aittala M, Hellsten J, Aila T (2020) Analyzing and improving the image quality of stylegan. In: IEEE Conference on computer vision and pattern recognition

  28. Chen W-T, Ding J-J, Kuo S-Y (2019) Pms-net: Robust haze removal based on patch map for single images. In: IEEE Conference on computer vision and pattern recognition

  29. Yang X, Xu Z, Luo J (2018) Towards perceptual image dehazing by physics-based disentanglement and adversarial training. In: AAAI Conference on artificial intelligence

  30. Wang T, Zhao L, Huang P, Zhang X, Xu J (2021) Haze concentration adaptive network for image dehazing. Neurocomputing 439:75–85. https://doi.org/10.1016/j.neucom.2021.01.042

    Article  Google Scholar 

  31. Guo F, Zhao X, Tang J, Peng H, Liu L, Zou B (2020) Single image dehazing based on fusion strategy. Neurocomputing 378:9–23. https://doi.org/10.1016/j.neucom.2019.09.094

    Article  Google Scholar 

  32. Huang P, Zhao L, Jiang R, Wang T, Zhang X (2021) Self-filtering image dehazing with self-supporting module. Neurocomputing 432:57–69. https://doi.org/10.1016/j.neucom.2020.11.039

    Article  Google Scholar 

  33. Zhang S, He F, Ren W (2020) Nldn: Non-local dehazing network for dense haze removal. Neurocomputing 410:363–373. https://doi.org/10.1016/j.neucom.2020.06.041

    Article  Google Scholar 

  34. Zheng Y, Zhan J, He S, Dong J, Du Y (2023) Curricular Contrastive Regularization for Physics-aware Single Image Dehazing. arXiv:2110.08020 Accessed 05-13-2023

  35. Chen Z, Wang Y, Yang Y, Liu D (2021) Psd: Principled synthetic-to-real dehazing guided by physical priors. In: Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition (CVPR), pp 7180–7189

  36. Li B, Liu X, Hu P, Wu Z, Lv J, Peng X (2022) All-In-One Image Restoration for Unknown Corruption. In: IEEE Conference on computer vision and pattern recognition, New Orleans, LA

  37. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139–144

    Article  MathSciNet  Google Scholar 

  38. Wang X, Yu K, Wu S, Gu J, Liu Y, Dong C, Qiao Y, Change Loy C (2018) Esrgan: Enhanced super-resolution generative adversarial networks. In: European conference on computer vision workshops, pp 0–0

  39. Wang X, Li Y, Zhang H, Shan Y (2021) Towards real-world blind face restoration with generative facial prior. In: IEEE Conference on computer vision and pattern recognition, pp 9168–9178

  40. Karras T, Laine S, Aittala M, Hellsten J, Lehtinen J, Aila T (2020) Analyzing and improving the image quality of stylegan. In: IEEE Conference on computer vision and pattern recognition, pp 8110–8119

  41. Li B, Ren W, Fu D, Tao D, Feng D, Zeng W, Wang Z (2018) Benchmarking single image dehazing and beyond. IEEE Trans Image Process

  42. Liu F, Shen C, Lin G, Reid I (2016) Learning depth from single monocular images using deep convolutional neural fields. IEEE Trans Pattern Anal Mach Intell 38(10):2024–2039

    Article  Google Scholar 

  43. Zhang J, Tao D (2020) Famed-net: A fast and accurate multi-scale end-to-end dehazing network. IEEE Trans Image Process 29(1):72–84

    Article  MathSciNet  Google Scholar 

  44. Qin X, Wang Z, Bai Y, Xie X, Xie H (2020) Ffa-net: Feature fusion attention network for single image dehazing. In: AAAI Conference on artificial intelligence

  45. Dong H, Pan J, Xiang L, Hu Z, Zhang X, Wang F, Yang MH (2020) Multi-scale boosted dehazing network with dense feature fusion. In: IEEE Conference on computer vision and pattern recognition

  46. Zhu Q, Mai J, Shao L (2020) A fast single image haze removal algorithm using color attenuation prior. IEEE Trans Image Process 24(11):3522–3533

    MathSciNet  Google Scholar 

  47. Wu H, Qu Y, Lin S, Zhou J, Qiao R, Zhang Z, Xie Y, Ma L (2021) Contrastive learning for compact single image dehazing. In: IEEE Conference on computer vision and pattern recognition, pp 10551–10560

  48. Bai H, Pan J, Xiang X, Tang J (2022) Self-guided image dehazing using progressive feature fusion. IEEE Trans Image Process 31:1217–1229

    Article  Google Scholar 

  49. Liu X, Ma Y, Shi Z, Chen J (2019) Griddehazenet: Attention-based multi-scale network for image dehazing. In: IEEE Int Conf Comput Vision

  50. Deng Z, Zhu L, Hu X, Fu C-W, Xu X, Zhang Q, Qin J, Heng P-A (2019) Deep multi-model fusion for single-image dehazing. In: IEEE International conference on computer vision, pp 2453–2462

  51. Wu R-Q, Duan Z-P, Guo C-L, Chai Z, Li C-Y (2023) RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors. arXiv:2304.03994 Accessed 05-18-2023

Download references

Funding

Shengdong Zhang is supported by the National Natural Science Foundation of China (Nos. U2033210, 82261138629, and 62271321). Shengdong Zhang is partially supported by the Science Project of Shaoxing University (Nos. 2022LG006, 20205048, and 20210026), the Science and Technology Plan Project in Basic Public Welfare class of Shaoxing city (No.2022A1).

Author information

Author notes

  1. Shengdong Zhang and Xiaoqin Zhang contributed equally to this work.

Authors and Affiliations

  1. Key Laboratory of Intelligent Informatics for Safety and Emergency of Zhejiang Province, Wenzhou University, Ouhai, Wenzhou, 325035, Zhejiang, China

    Shengdong Zhang & Xiaoqin Zhang

  2. Computer Science Engineering Department, Shaoxing University, Yuecheng, Shaoxing, 312000, Zhejiang, China

    Shengdong Zhang & En Fan

  3. College of Computer Science and Software Engineering, Shenzhen University, Nanshan, Shenzhen, 518060, Guangdong, China

    Linlin Shen

Authors
  1. Shengdong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoqin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Linlin Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. En Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shengdong Zhang.

Ethics declarations

Conflicts of interest

The authors have no relevant financial or non-financial interests to disclose.

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

Zhang, S., Zhang, X., Shen, L. et al. GAN-based dehazing network with knowledge transferring. Multimed Tools Appl 83, 45095–45110 (2024). https://doi.org/10.1007/s11042-023-17226-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-17226-4

Keywords

Search

Navigation