Skip to main content
Log in

Degradation-adaptive neural network for jointly single image dehazing and desnowing

  • Letter
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

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

Access this article

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.

References

  1. Qin X, Wang Z, Bai Y, Xie X, Jia H. FFA-Net: feature fusion attention network for single image dehazing. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence. 2020, 11908–11915

  2. Chen Z, Wang Y, Yang Y, Liu D. PSD: principled synthetic-to-real dehazing guided by physical priors. In: Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021, 7180–7189

  3. Liu Y, Zhu L, Pei S, Fu H, Qin J, Zhang Q, Wan L, Feng W. From synthetic to real: image dehazing collaborating with unlabeled real data. In: Proceedings of the 29th ACM International Conference on Multimedia. 2021, 50–58

  4. Liu Y, Yan Z, Tan J, Li Y. Multi-purpose oriented single nighttime image haze removal based on unified variational retinex model. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 33(4): 1643–1657

    Article  Google Scholar 

  5. Liu Y F, Jaw D W, Huang S C, Hwang J N. DesnowNet: context-aware deep network for snow removal. IEEE Transactions on Image Processing, 2018, 27(6): 3064–3073

    Article  MathSciNet  Google Scholar 

  6. Chen W T, Fang H Y, Ding J J, Tsai C C, Kuo S Y. JSTASR: joint size and transparency-aware snow removal algorithm based on modified partial convolution and veiling effect removal. In: Proceedings of the 16th European Conference on Computer Vision. 2020, 754–770

  7. Chen W T, Fang H Y, Hsieh C L, Tsai C C, Chen I H, Ding J J, Kuo S Y. ALL snow removed: single image desnowing algorithm using hierarchical dual-tree complex wavelet representation and contradict channel loss. In: Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. 2021, 4196–4205

  8. Li R, Tan R T, Cheong L F. All in one bad weather removal using architectural search. In: Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020, 3175–3185

  9. Pavlitskaya S, Hubschneider C, Weber M, Moritz R, Hüger F, Schlicht P, Zöllner J M. Using mixture of expert models to gain insights into semantic segmentation. In: Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2020, 342–343

  10. Li B, Ren W, Fu D, Tao D, Feng D, Zeng W, Wang Z. Benchmarking single-image dehazing and beyond. IEEE Transactions on Image Processing, 2019, 28(1): 492–505

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 62301453), the Natural Science Foundation of Chongqing, China (cstc2020jcyj-msxmX0324), and the Natural Science Foundation of Fujian, China (2021J01867).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yun Liu.

Ethics declarations

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

Additional information

Supporting information The supporting infomation is available online at joural.hep.com.cn and link.springer.com.

Electronic Supplementary Materials

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, E., Chen, S., Ye, T. et al. Degradation-adaptive neural network for jointly single image dehazing and desnowing. Front. Comput. Sci. 18, 182707 (2024). https://doi.org/10.1007/s11704-023-2764-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11704-023-2764-y

Navigation