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Feature aggregation and modulation network for single image dehazing

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Abstract

Deep learning-based methods have recently achieved satisfying results in image dehazing. However, we observe that various researchers devote themselves to learning haze-free images directly, while often paying no attention to the physical features of the hazy image formation process. For single image dehazing, a suitable transmission map and global atmospheric light guidance proved effective. Meanwhile, for many dehazing networks, deep and non-adjacent feature information is not utilized which can likewise affect the effectiveness of image recovery. Therefore, we develop an effective feature aggregation and modulation network for image dehazing called FAM-Net. Specifically, the proposed FAM-Net first uses CNN to estimate the transmission map and global atmospheric light, and then embeds the output features into the overall network for joint dehazing. A feature aggregation and modulation module is proposed to fuse the extracted features of atmospheric light and transmission map into the network. Moreover, the attention guidance aggregation module is designed as a replacement for the skip connection. Furthermore, a novel edge-preserving loss function is proposed for training the network, preserving more details of the reconstructed images. Experimental results indicate that FAM-Net outperforms existing dehazing methods in quantitative and qualitative aspects.

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References

  1. He K, Sun J, Tang X (2010) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341–2353

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  3. Qin X, Wang Z, Bai Y et al (2020) Ffa-net: Feature fusion attention network for single image dehazing. In: Proceedings of the AAAI conference on artificial intelligence, pp 11908–11915

  4. McCartney EJ (1976) Optics of the atmosphere: scattering by molecules and particles. New York

  5. Narasimhan SG, Nayar SK (2000) Chromatic framework for vision in bad weather. In: Proceedings IEEE conference on computer vision and pattern recognition. CVPR 2000 (Cat. No. PR00662), IEEE, pp 598–605

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

    Article  MathSciNet  Google Scholar 

  7. Berman D, Treibitz T, Avidan S (2017) Air-light estimation using haze-lines. In: 2017 IEEE International conference on computational photography (ICCP), IEEE, pp 1–9

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

  9. Kar A, Dhara SK, Sen D et al (2020) Progressive update guided interdependent networks for single image dehazing. arXiv:2008.01701

  10. Dong J, Pan J (2020) Physics-based feature dehazing networks. In: European conference on computer vision, Springer, pp 188–204

  11. Ren W, Ma L, Zhang J et al (2018) Gated fusion network for single image dehazing. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3253–3261

  12. Chen D, He M, Fan Q et al (2019) Gated context aggregation network for image dehazing and deraining. In: 2019 IEEE winter conference on applications of computer vision (WACV), IEEE, pp 1375–1383

  13. Zhao S, Zhang L, Shen Y et al (2021) Refinednet: A weakly supervised refinement framework for single image dehazing. IEEE Trans Image Process 30:3391–3404

    Article  Google Scholar 

  14. Liu X, Ma Y, Shi Z et al (2019) Griddehazenet: Attention-based multi-scale network for image dehazing. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 7314–7323

  15. Guo CL, Yan Q, Anwar S et al (2022) Image dehazing transformer with transmission-aware 3d position embedding. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 5812–5820

  16. Ren W, Pan J, Zhang H et al (2020) Single image dehazing via multi-scale convolutional neural networks with holistic edges. Int J Comput Vis 128(1):240–259

    Article  Google Scholar 

  17. Pang Y, Xie J, Li X (2018) Visual haze removal by a unified generative adversarial network. IEEE Trans Circ Syst Vid Tech 29(11):3211–3221

    Article  Google Scholar 

  18. Li B, Peng X, Wang Z et al (2017) Aod-net: All-in-one dehazing network. In: Proceedings of the IEEE international conference on computer vision, pp 4770–4778

  19. Zhu H, Peng X, Chandrasekhar V et al (2018) Dehazegan: When image dehazing meets differential programming. In: IJCAI, pp 1234–1240

  20. Zhang J, Tao D (2019) Famed-net: A fast and accurate multi-scale end-to-end dehazing network. IEEE Trans Image Process 29:72–84

    Article  MathSciNet  Google Scholar 

  21. Riaz I, Yu T, Rehman Y et al (2016) Single image dehazing via reliability guided fusion. J Vis Commun Image Represent 40:85–97

    Article  Google Scholar 

  22. Dong H, Pan J, Xiang L et al (2020) 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

  23. Wang C, Shen HZ, Fan F et al (2021) Eaa-net: A novel edge assisted attention network for single image dehazing. Knowl-Based Syst 228:107279

    Article  Google Scholar 

  24. Zhang X, Wang T, Wang J et al (2020) Pyramid channel-based feature attention network for image dehazing. Comput Vis Image Underst 197:103003

    Article  Google Scholar 

  25. Wang T, Zhao L, Huang P et al (2021) Haze concentration adaptive network for image dehazing. Neurocomputing 439:75–85

    Article  Google Scholar 

  26. Xiao B, Zheng Z, Zhuang Y et al (2022) Single uhd image dehazing via interpretable pyramid network. Available at SSRN 4134196

  27. Wang Z, Cun X, Bao J et al (2022) Uformer: A general u-shaped transformer for image restoration. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 17683–17693

  28. Song Y, He Z, Qian H et al (2023) Vision transformers for single image dehazing. IEEE Trans Image Process 32:1927–1941

    Article  Google Scholar 

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

  30. Ye T, Zhang Y, Jiang M et al (2022) Perceiving and modeling density for image dehazing. In: Part XIX (ed) Computer vision-ECCV 2022: 17th European conference, Tel Aviv, Israel, October 23–27, 2022, proceedings. Springer, pp 130–145

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  32. Deng Z, Zhu L, Hu X et al (2019) Deep multi-model fusion for single-image dehazing. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 2453–2462

  33. Liu Y, Zhu L, Pei S et al (2021) From synthetic to real: Image dehazing collaborating with unlabeled real data. In: Proceedings of the 29th ACM international conference on multimedia, pp 50–58

  34. Yu Y, Liu H, Fu M et al (2021) A two-branch neural network for non-homogeneous dehazing via ensemble learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 193–202

  35. Zhao S, Zhang L, Huang S et al (2020) Dehazing evaluation: Real-world benchmark datasets, criteria, and baselines. IEEE Trans Image Process 29:6947–6962

    Article  MathSciNet  Google Scholar 

  36. Zhang R, Isola P, Efros AA et al (2018) The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 586–595

  37. Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980

  38. He T, Zhang Z, Zhang H et al (2019) Bag of tricks for image classification with convolutional neural networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 558–567

  39. Ren W, Liu S, Zhang H et al (2016) Single image dehazing via multi-scale convolutional neural networks. In: European conference on computer vision, Springer, pp 154–169

  40. Nishino K, Kratz L, Lombardi S (2012) Bayesian defogging. Int J Comput Vis 98:263–278

    Article  Google Scholar 

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

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 12075090), and by the Funding by Science and Technology Projects in Guangzhou (Grant No. 2023A04J1686), and by the GuangDong Basic and Applied Basic Research Foundation (Grant No. 2022A1515110119).

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  1. Fei Tan and Xiaoyuan Yu contributed equally to this work.

Authors and Affiliations

  1. School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou, 510006, China

    Fei Tan, Xiaoyuan Yu, Renjie Wang, Baoquan Ai & Fengguo Li

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  1. Fei Tan
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  2. Xiaoyuan Yu
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  3. Renjie Wang
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  4. Baoquan Ai
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Correspondence to Fengguo Li.

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Tan, F., Yu, X., Wang, R. et al. Feature aggregation and modulation network for single image dehazing. Multimed Tools Appl 83, 50269–50287 (2024). https://doi.org/10.1007/s11042-023-17473-5

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