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Visibility restoration for real-world hazy images via improved physical model and Gaussian total variation

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References

  1. Zhu Q, Mai J, Shao L. A fast single image haze removal algorithm using color attenuation prior. IEEE Transactions on Image Processing, 2015, 24(11): 3522–3533

    Article  MathSciNet  Google Scholar 

  2. Li C, Yuan C, Pan H, Yang Y, Wang Z, Zhou H, Xiong H. Single image dehazing based on improved bright channel prior and dark channel prior. Electronics, 2023, 12(2): 299

    Article  Google Scholar 

  3. Ren W, Liu S, Zhang H, Pan J, Cao X, Yang M H. Single image dehazing via multi-scale convolutional neural networks. In: Proceedings of the 14th European Conference on Computer Vision. 2016, 154–169

  4. Cai B, Xu X, Jia K, Qing C, Tao D. DehazeNet: an end-to-end system for single image haze removal. IEEE Transactions on Image Processing, 2016, 25(11): 5187–5198

    Article  MathSciNet  Google Scholar 

  5. Yang D, Sun J. Proximal Dehaze-Net: a prior learning-based deep network for single image dehazing. In: Proceedings of the 15th European Conference on Computer Vision. 2018, 729–746

  6. Ye T, Zhang Y, Jiang M, Chen L, Liu Y, Chen S, Chen E. Perceiving and modeling density for image dehazing. In: Proceedings of the 17th European Conference on Computer Vision. 2022, 130–145

  7. Hao S, Han X, Guo Y, Xu X, Wang M. Low-light image enhancement with semi-decoupled decomposition. IEEE Transactions on Multimedia, 2020, 22(12): 3025–3038

    Article  Google Scholar 

  8. Choi L K, You J, Bovik A C. Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Transactions on Image Processing, 2015, 24(11): 3888–3901

    Article  MathSciNet  Google Scholar 

  9. Hautière N, Tarel J P, Aubert D, Dumont E. Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Analysis and Stereology, 2008, 27(2): 87–95

    Article  MathSciNet  Google Scholar 

  10. Mittal A, Soundararajan R, Bovik A C. Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters, 2013, 20(3): 209–212

    Article  Google Scholar 

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 62301453) and the Natural Science Foundation of Chongqing, China (No. cstc2020jcyj-msxmX0324).

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Authors and Affiliations

  1. College of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Chuan Li & Hao Zhou

  2. School of Big Data and Intelligent Engineering, Chongqing College of International Business and Economics, Chongqing, 401520, China

    Chuan Li

  3. College of Artificial Intelligence, Southwest University, Chongqing, 400715, China

    Enping Hu, Xinyu Zhang & Yun Liu

  4. College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China

    Hailing Xiong

Authors
  1. Chuan Li
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  2. Enping Hu
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  3. Xinyu Zhang
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  4. Hao Zhou
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  5. Hailing Xiong
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  6. Yun Liu
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Corresponding author

Correspondence to Yun Liu.

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Competing interests The authors declare that they have no competing interests or financial conflicts to disclose.

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Li, C., Hu, E., Zhang, X. et al. Visibility restoration for real-world hazy images via improved physical model and Gaussian total variation. Front. Comput. Sci. 18, 181708 (2024). https://doi.org/10.1007/s11704-023-3394-0

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  • DOI: https://doi.org/10.1007/s11704-023-3394-0

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