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Single-image dehazing via depth-guided deep retinex decomposition

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Abstract

In this paper, we explore the problem of single-image haze removal based on retinex model and new deep retinex decomposition architecture. Reformulating dehazing as reverse retinex, we propose a depth-guided retinex decomposition network, which consists of the Decom-Net, with two-branches for the retinex decomposition on the reversed hazy image, and the Guide-Net, with depth information for guiding the estimation of ideal illumination. To promote the accuracy of retinex decomposition, we develop an effective boosted decoder with a fusion attention mechanism to optimize the illumination iteratively, giving rise to a refined reflectance. Additionally, due to the reversible relationship between haze and low-light images, our network could effectively realize dehazing in nighttime. Through sets of experiments on a variety of synthetic and natural images, we validate the effectiveness of the proposed model in haze removal, competitively in terms of visual appearance and metrics, considering both daytime and nighttime cases.

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References

  1. Zhang, J., Li, L., Zhang, Y., Yang, G., Cao, X., Sun, J.: Video dehazing with spatial and temporal coherence. Vis. Comput. 27(6), 749–757 (2011)

    Article  Google Scholar 

  2. Li, B., Peng, X., Wang, Z., Xu, J., Feng, D.: End-to-end united video dehazing and detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)

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

    Article  Google Scholar 

  4. Tan, R.T.: Visibility in bad weather from a single image. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008). IEEE

  5. Zhang, S., He, F.: Drcdn: learning deep residual convolutional dehazing networks. Vis. Comput. 36(9), 1797–1808 (2020)

    Article  Google Scholar 

  6. 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 

  7. 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  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  9. 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)

  10. Jobson, D.J., Rahman, Z.-U., Woodell, G.A.: Properties and performance of a center/surround retinex. IEEE Trans. Image Process. 6(3), 451–462 (1997)

    Article  Google Scholar 

  11. Jobson, D.J., Rahman, Z.-U., Woodell, G.A.: A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image Process. 6(7), 965–976 (1997)

    Article  Google Scholar 

  12. 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  MATH  Google Scholar 

  13. 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)

  14. 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)

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

  16. 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)

  17. 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)

  18. 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 

  19. Kim, S.E., Park, T.H., Eom, I.K.: Fast single image dehazing using saturation based transmission map estimation. IEEE Trans. Image Process. 29, 1985–1998 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  20. Galdran, A., Alvarez-Gila, A., Bria, A., Vazquez-Corral, J., Bertalmío, M.: On the duality between retinex and image dehazing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8212–8221 (2018)

  21. Li, P., Tian, J., Tang, Y., Wang, G., Wu, C.: Deep retinex network for single image dehazing. IEEE Trans. Image Process. 30, 1100–1115 (2020)

    Article  Google Scholar 

  22. Rahman, Z.-U., Jobson, D.J., Woodell, G.A.: Retinex processing for automatic image enhancement. J. Electron. Imaging 13(1), 100–110 (2004)

    Article  Google Scholar 

  23. Guo, X., Li, Y., Ling, H.: Lime: low-light image enhancement via illumination map estimation. IEEE Trans. Image Process. 26(2), 982–993 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  24. Romano, Y., Elad, M.: Boosting of image denoising algorithms. SIAM J. Imaging Sci. 8(2), 1187–1219 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  25. Chen, C., Xiong, Z., Tian, X., Wu, F.: Deep boosting for image denoising. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–18 (2018)

  26. Dong, H., Pan, J., Xiang, 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)

  27. Qin, X., Wang, Z., Bai, Y., Xie, X., Jia, H.: Ffa-net: Feature fusion attention network for single image dehazing. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 11908–11915 (2020)

  28. Li, X., Hua, Z., Li, J.: Attention-based adaptive feature selection for multi-stage image dehazing. Vis. Comput., 1–16 (2022)

  29. Li, L., Pan, J., Lai, W.-S., Gao, C., Sang, N., Yang, M.-H.: Dynamic scene deblurring by depth guided model. IEEE Trans. Image Process. 29, 5273–5288 (2020)

    Article  MATH  Google Scholar 

  30. 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  MATH  Google Scholar 

  31. Zhang, J., Cao, Y., Zha, Z.-J., Tao, D.: Nighttime dehazing with a synthetic benchmark. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 2355–2363 (2020)

  32. Tarel, J.-P., Hautiere, N.: Fast visibility restoration from a single color or gray level image. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2201–2208 (2009). IEEE

  33. Zhang, J., Cao, Y., Wang, Z.: Nighttime haze removal based on a new imaging model. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 4557–4561 (2014). IEEE

  34. Li, Y., Tan, R.T., Brown, M.S.: Nighttime haze removal with glow and multiple light colors. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 226–234 (2015)

  35. Zhang, J., Cao, Y., Fang, S., Kang, Y., Wen Chen, C.: Fast haze removal for nighttime image using maximum reflectance prior. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7418–7426 (2017)

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No. 61672122) and Natural Science Foundation of China (61802045).

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  1. Hui Chen and Long Ma have equally contributed to this work.

Authors and Affiliations

  1. The College of Information Science and Technology, Dalian Maritime University, Linghai Road, Dalian, Liaoning, 116026, China

    Hui Chen, Rong Chen & Nannan Li

  2. DUT-RU International School of Information Science and Engineering, Dalian University of Technology, Linghai Road, Dalian, Liaoning, 116026, China

    Long Ma

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  1. Hui Chen
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Correspondence to Rong Chen or Nannan Li.

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Chen, H., Chen, R., Ma, L. et al. Single-image dehazing via depth-guided deep retinex decomposition. Vis Comput 39, 5279–5291 (2023). https://doi.org/10.1007/s00371-022-02659-z

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