DRCDN: learning deep residual convolutional dehazing networks

  • Shengdong Zhang
  • Fazhi HeEmail author
Original Article


Single image dehazing, which is the process of removing haze from a single input image, is an important task in computer vision. This task is extremely challenging because it is massively ill-posed. In this paper, we propose a novel end-to-end deep residual convolutional dehazing network (DRCDN) based on convolutional neural networks for single image dehazing, which consists of two subnetworks: one network is used for recovering a coarse clear image, and the other network is used to refine the result. The DRCDN firstly predicts the coarse clear image via a context aggregation subnetwork, which can capture global structure information. Subsequently, it adopts a novel hierarchical convolutional neural network to further refine the details of the clean image by integrating the local context information. The DRCDN is directly trained using complete images and the corresponding ground-truth haze-free images. Experimental results on synthetic datasets and natural hazy images demonstrate that the proposed method performs favorably against the state-of-the-art methods.


Residual learning Dehazing Image restoration Global structure information Deep learning 



We thank anonymous reviewers very much for their suggestive comments. This work is partially supported by the NSFC (No. 61472289, 41571436).

Compliance with ethical standards

Conflict of interest

The authors declares that there is no conflict of interest.


  1. 1.
    Narasimhan, S.G., Nayar, S.K.: Vision and the atmosphere. Int. J. Comput. Vision 48(3), 233–254 (2002)CrossRefGoogle Scholar
  2. 2.
    He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)CrossRefGoogle Scholar
  3. 3.
    Schechner, Y.Y., Narasimhan, S.G., Nayar, S.K : Instant dehazing of images using polarization. In: Computer Vision and Pattern Recognition, vol. 1, pp. 325–332 (2001)Google Scholar
  4. 4.
    Narasimhan, S.G., Nayar, S.K.: Contrast restoration of weather degraded images. IEEE Trans. Pattern Anal. Mach. Intell. 25(6), 713–724 (2003)CrossRefGoogle Scholar
  5. 5.
    Shwartz, S., Namer, E., Schechner, Y.Y : Blind haze separation. In: Computer Vision and Pattern Recognition, vol. 2, pp. 1984–1991 (2006)Google Scholar
  6. 6.
    Kopf, J., Neubert, B., Chen, B., Cohen, M., Cohen-Or, D., Deussen, O., Uyttendaele, M., Lischinski, D.: Deep photo: model-based photograph enhancement and viewing. In: ACM transactions on graphics, vol. 27, Article No. 116 (2008)CrossRefGoogle Scholar
  7. 7.
    Chen, X., He, F.: A matting method based on full feature coverage. Multimedia Tools Appl. 78(9), 11173–11201 (2019)CrossRefGoogle Scholar
  8. 8.
    Yu, H., He, F.: A novel segmentation model for medical images with intensity inhomogeneity based on adaptive perturbation. Multimedia Tools Appl. 78(9), 11779–11798 (2019)CrossRefGoogle Scholar
  9. 9.
    Haiping, Y., He, F., Pan, Y.: A novel region-based active contour model via local patch similarity measure for image segmentation. Multimedia Tools Appl. 77(18), 24097–24119 (2018)CrossRefGoogle Scholar
  10. 10.
    Tan, R.T: Visibility in bad weather from a single image. In: Computer Vision and Pattern Recognition (2008)Google Scholar
  11. 11.
    Fattal, R.: Single image dehazing. ACM Trans. Gr. 27(3), 72 (2008)CrossRefGoogle Scholar
  12. 12.
    Meng, G., Wang, Y., Duan, J., Xiang, S., Pan, C.: Efficient image dehazing with boundary constraint and contextual regularization. In: International Conference on Computer Vision, pp. 617–624 (2013)Google Scholar
  13. 13.
    Fattal, R.: Dehazing using color-lines. ACM Trans. Gr. 34(1), 13 (2014)CrossRefGoogle Scholar
  14. 14.
    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)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Berman, D., Avidan, S., et al.: Non-local image dehazing. In: Computer Vision and Pattern Recognition, pp. 1674–1682 (2016)Google Scholar
  16. 16.
    Li, K., He, F., Haiping, Y., Chen, X.: A parallel and robust object tracking approach synthesizing adaptive bayesian learning and improved incremental subspace learning. Front. Comput. Sci. 13(5), 1116–1135 (2019)CrossRefGoogle Scholar
  17. 17.
    Ren, W., Liu, S., Ma, L., Qianqian, X., Xiangyu, X., Cao, X., Junping, D., Yang, M.-H.: Low-light image enhancement via a deep hybrid network. IEEE Trans. Image Process. 28(9), 4364–4375 (2019)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Ren, W., Zhang, J., Ma, L., Pan, J., Cao, X., Zuo, W., Liu, W., Yang, M.-H.: Deep non-blind deconvolution via generalized low-rank approximation. In: Advances in Neural Information Processing Systems, pp. 297–307 (2018)Google Scholar
  19. 19.
    Li, H., He, F., Yan, X.: IBEA-SVM an indicator-based evolutionary algorithm based on pre-selection with classification guided by SVM. Appl. Math.-A J. Chin. Univ. 34(1), 1–26 (2019)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Li, H., He, F., Liang, Y., Quan, Q.: A dividing-based many-objective evolutionary algorithm for large-scale feature selection. Soft Comput. (2019). CrossRefGoogle Scholar
  21. 21.
    Yan, Y., Ren, W., Cao, X.: Recolored image detection via a deep discriminative model. IEEE Trans. Inf. Forensics Secur. 14(1), 5–17 (2018)CrossRefGoogle Scholar
  22. 22.
    Ding, B., Long, C., Zhang, L., Xiao, C.: ARGAN: attentive recurrent generative adversarial network for shadow detection and removal. In: International Conference on Computer Vision (2019)Google Scholar
  23. 23.
    Yong, J., He, F., Li, H., Zhou, W.: A novel bat algorithm based on cross boundary learning and uniform explosion strategy. Appl. Math.-A J. Chin. Univ. (2019). CrossRefGoogle Scholar
  24. 24.
    Luo, J., He, F., Yong, J.: An efficient and robust bat algorithm with fusion of opposition-based learning and whale optimization algorithm. Intell. Data Anal. 24(3: to appear in this issue) (2020)Google Scholar
  25. 25.
    Zhang, W., Xiao, C: PCAN: 3D attention map learning using contextual information for point cloud based retrieval. In: the IEEE Conference on Computer Vision and Pattern Recognition, pp. 12436–12445 (2019)Google Scholar
  26. 26.
    Hou, N., He, F., Zhou, Y., Chen, Y.: An efficient GPU-based parallel tabu search algorithm for hardware/software co-design. Front. Comput. Sci. (2020).
  27. 27.
    Cai, B., Xiangmin, 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)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., Yang, M.-H.: Single image dehazing via multi-scale convolutional neural networks. In: European Conference on Computer Vision, pp. 154–169 (2016)CrossRefGoogle Scholar
  29. 29.
    Sulami, M., Glatzer, I., Fattal, R., Werman, M.: Automatic recovery of the atmospheric light in hazy images. In: IEEE International Conference on Computational Photography, pp. 1–11 (2014)Google Scholar
  30. 30.
    Berman, D., Treibitz, T., Avidan, S.: Air-light estimation using haze-lines. In: IEEE International Conference on Computational Photography, pp. 1–9 (2017)Google Scholar
  31. 31.
    Li, B., Peng, X., Wang, Z., Xu, J., Feng, D.: Aod-net: all-in-one dehazing network. In: International Conference on Computer Vision, pp. 4770–4778 (2017)Google Scholar
  32. 32.
    Ren, W., Ma, L., Zhang, J., Pan, J., Cao, X., Liu, W., Yang, M.-H.: Gated fusion network for single image dehazing. In: Computer Vision and Pattern Recognition, pp. 3253–3261 (2018)Google Scholar
  33. 33.
    Zhang, S., Ren, W., Yao, J.: Feed-net: Fully end-to-end dehazing. In: IEEE International Conference on Multimedia and Expo, pp. 1–6 (2018)Google Scholar
  34. 34.
    Zhang, H., Patel, V.M.: Densely connected pyramid dehazing network. In: Computer Vision and Pattern Recognition, pp. 3194–3203 (2018)Google Scholar
  35. 35.
    Ancuti, C.O., Ancuti, C.: Single image dehazing by multi-scale fusion. IEEE Trans. Image Process. 22(8), 3271–3282 (2013)CrossRefGoogle Scholar
  36. 36.
    Tarel, J.-P., Hautiere, N.: Fast visibility restoration from a single color or gray level image. In: International Conference on Computer Vision, pp. 2201–2208 (2009)Google Scholar
  37. 37.
    Tang, K., Yang, J., Wang, J.: Investigating haze-relevant features in a learning framework for image dehazing. In: Computer Vision and Pattern Recognition, pp. 2995–3000 (2014)Google Scholar
  38. 38.
    Pan, Y., He, F., Yu, H.: A correlative denoising autoencoder to model social influence for top-n recommender system. Front. Comput. Sci. (2019). CrossRefGoogle Scholar
  39. 39.
    Pan, Y., He, F., Yu, H.: Learning adaptive trust strength with user roles of truster and trustee for trust-aware recommender systems. Appl. Intell. (2019). CrossRefGoogle Scholar
  40. 40.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  41. 41.
    Szegedy, C., Toshev, A., Erhan, D.: Deep neural networks for object detection. In: Advances in Neural Information Processing Systems, pp. 2553–2561 (2013)Google Scholar
  42. 42.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)Google Scholar
  43. 43.
    Yu, J., Jiang, Y., Wang, Z., Cao, Z., Huang, T.: Unitbox: an advanced object detection network. In: Proceedings of the 2016 ACM on Multimedia Conference, pp. 516–520 (2016)Google Scholar
  44. 44.
    Xie, J., Xu, L., Chen, E.: Image denoising and inpainting with deep neural networks. In: Advances in Neural Information Processing Systems, pp. 341–349 (2012)Google Scholar
  45. 45.
    Dong, C., Loy, C.C., He, K., Tang, X.: Xiaoou: image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016)CrossRefGoogle Scholar
  46. 46.
    Kim, J., Kwon Lee, J., Mu Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: Computer Vision and Pattern Recognition, pp. 1646–1654 (2016)Google Scholar
  47. 47.
    Liu, D., Wen, B., Liu, X., Huang, T.S.: When image denoising meets high-level vision tasks: a deep learning approach. In: International Joint Conferences on Artificial Intelligence, pp. 842–848 (2017)Google Scholar
  48. 48.
    Zhang, S., He, F., Ren, W., Yao, J.: Joint learning of image detail and transmission map for single image dehazing. Vis. Comput. (2018). CrossRefGoogle Scholar
  49. 49.
    Yu, F., Koltun, V., Funkhouser, T.A: Dilated residual networks. In:Computer Vision and Pattern Recognition, vol. 2, p. 3 (2017)Google Scholar
  50. 50.
    Fu, X., Huang, J., Zeng, D., Huang, Y., Ding, X., Paisley, J.: Removing rain from single images via a deep detail network. In:Computer Vision and Pattern Recognition, pp. 3855–3863 (2017)Google Scholar
  51. 51.
    Mehta, S., Rastegari, M., Caspi, A., Shapiro, L., Hajishirzi, H.: Espnet: efficient spatial pyramid of dilated convolutions for semantic segmentation. In: European Conference on Computer Vision, pp. 552–568 (2018)CrossRefGoogle Scholar
  52. 52.
    Yu, Fi., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: International Conference on Learning Representations (2016). arXiv:1511.07122
  53. 53.
    Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: European Conference on Computer Vision, pp. 694–711 (2016)CrossRefGoogle Scholar
  54. 54.
    Russakovsky, O., Deng, J., Hao, S., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  55. 55.
    Zhang, Y., Ding, L., Sharma, G.: Hazerd: an outdoor scene dataset and benchmark for single image dehazing. In: International Conference on Image Processing, pp. 3205–3209 (2017)Google Scholar
  56. 56.
    Li, K., He, F., Yu, H.: Robust visual tracking based on convolutional features with illumination and occlusion handing. J. Comput. Sci. Technol. 33(1), 223–236 (2018)CrossRefGoogle Scholar
  57. 57.
    Mbelwa, J.T., Zhao, Q., Wang, F.: Visual tracking tracker via object proposals and co-trained kernelized correlation filters. Vis. Comput. (2019). CrossRefGoogle Scholar
  58. 58.
    Pan, Y., He, F., Haiping, Y.: A novel enhanced collaborative autoencoder with knowledge distillation for top-n recommender systems. Neurocomputing 332, 137–148 (2019)CrossRefGoogle Scholar
  59. 59.
    Liu, F., Shen, C., Lin, G., Reid, I.: Learning depth from single monocular images using deep convolutional neural fields. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 2024–2039 (2016)CrossRefGoogle Scholar
  60. 60.
    Li, B., Ren, W., Fu, D., Tao, D., Feng, D., Zeng, W., Wang, Z.: Benchmarking single image dehazing and beyond. In: IEEE Transactions on Image Processing, pp. 492–505 (2018)MathSciNetCrossRefGoogle Scholar
  61. 61.
    Yang, D., Sun, J.: Proximal dehaze-net: A prior learning-based deep network for single image dehazing. In: European Conference on Computer Vision, pp. 702–717 (2018)Google Scholar
  62. 62.
    FazlErsi, E., Kazemi Nooghabi, M.: Revisiting correlation based filters for low-resolution and long-term visual tracking. Vis. Comput. 35(10), 1447–1459 (2019)CrossRefGoogle Scholar
  63. 63.
    Doyle, L., David Mould, D.: Augmenting photographs with textures using the laplacian pyramid. Vis. Comput. 35(10), 1489–1500 (2019)CrossRefGoogle Scholar
  64. 64.
    Umer, S., Dhara, B.C., Chanda, B.: NIR and VW iris image recognition using ensemble of patch statistics features. Vis. Comput. 35(9), 1327–1344 (2019)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer ScienceWuhan UniversityWuhanChina

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