Abstract
Single image dehazing is a challenging problem that aims to recover a high-quality haze-free image from a hazy image. In this paper, we propose an U-Net like deep network with contiguous memory residual blocks and gated fusion sub-network module to deal with the single image dehazing problem. The contiguous memory residual block is used to increase the flow of information by feature reusing and a gated fusion sub-network module is used to better combine the features of different levels. We evaluate our proposed method using two public image dehazing benchmarks. The experiments demonstrate that our network can achieve a state-of-the-art performance when compared with other popular methods.
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References
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 (2016). https://doi.org/10.1109/TIP.2016.2598681
Cantor, A.: Optics of the atmosphere-scattering by molecules and particles. IEEE J. Quantum Electron. 14(9), 698–699 (1978)
Chen, C., Do, M.N., Wang, J.: Robust image and video dehazing with visual artifact suppression via gradient residual minimization. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 576–591. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_36
Chen, D., et al.: Gated context aggregation network for image dehazing and deraining. arXiv preprint arXiv:1811.08747 (2018)
Fan, Q., Yang, J., Hua, G., Chen, B., Wipf, D.: A generic deep architecture for single image reflection removal and image smoothing. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3238–3247 (2017)
Fattal, R.: Single image dehazing. ACM Trans. Graph. 27(3), 1–9 (2008)
Hautiàre, N., Tarel, J.P., Aubert, D.: Towards fog-free in-vehicle vision systems through contrast restoration. In: IEEE Conference on Computer Vision & Pattern Recognition (2007)
He, K., Jian, S., Tang, X.: Single image haze removal using dark channel prior. In: IEEE Conference on Computer Vision & Pattern Recognition (2009)
Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kopf, J., et al.: Deep photo:model-based photograph enhancement and viewing. ACM Trans. Graph. 27(5), 1–10 (2008)
Li, B., Peng, X., Wang, Z., Xu, J., Dan, F.: AOD-Net: All-in-One dehazing network. In: IEEE International Conference on Computer Vision (2017)
Li, B., et al.: Benchmarking single image dehazing and beyond. IEEE Trans. Image Process. 1 (2018). https://doi.org/10.1109/TIP.2018.2867951
Li, R., Pan, J., Li, Z., Tang, J.: Single image dehazing via conditional generative adversarial network, pp. 8202–8211 (2018). https://doi.org/10.1109/CVPR.2018.00856
Li, Z., Ping, T., Tan, R.T., Zou, D., Zhou, S.Z., Cheong, L.F.: Simultaneous video defogging and stereo reconstruction. In: Computer Vision & Pattern Recognition (2015)
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)
Mei, K., Jiang, A., Li, J., Wang, M.: Progressive feature fusion network for realistic image dehazing. CoRR abs/1810.02283 (2018). http://arxiv.org/abs/1810.02283
Meng, G., Ying, W., Duan, J., Xiang, S., Pan, C.: Efficient image dehazing with boundary constraint and contextual regularization. In: IEEE International Conference on Computer Vision (2014)
Narasimhan, S.G., Nayar, S.K.: Vision and the atmosphere. Int. J. Comput. Vis. 48(3), 233–254 (2002)
Narasimhan, S.G., Nayar, S.K.: Contrast restoration of weather degraded images. IEEE Trans. Pattern Anal. Mach. Intell. 25(6), 713–724 (2003)
Narasimhan, S.G., Nayar, S.K.: Chromatic framework for vision in bad weather. In: IEEE Conference on Computer Vision & Pattern Recognition (2002)
Pei, S.C., Lee, T.Y.: Nighttime haze removal using color transfer pre-processing and dark channel prior. In: IEEE International Conference on Image Processing (2012)
Qingsong, Z., Jiaming, M., Ling, S.: A fast single image haze removal algorithm using color attenuation prior. IEEE Trans. Image Process. 24(11), 3522–3533 (2015)
Ren, W., Cao, X.: Deep video dehazing. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds.) PCM 2017. LNCS, vol. 10735, pp. 14–24. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77380-3_2
Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., Yang, M.-H.: Single image dehazing via multi-scale convolutional neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 154–169. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_10
Ren, W., et al.: Gated fusion network for single image dehazing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3253–3261 (2018)
Schechner, Y.Y., Narasimhan, S.G., Nayar, S.K.: Instant dehazing of images using polarization. In: Proceedings IEEE Conference Computer Vision & Pattern Recognition, vol. 1, p. 325 (2001)
Shwartz, S., Namer, E., Schechner, Y.Y.: Blind haze separation. In: IEEE Computer Society Conference on Computer Vision & Pattern Recognition (2006)
Tan, R.T.: Visibility in bad weather from a single image. In: IEEE Conference on Computer Vision & Pattern Recognition (2008)
Treibitz, T., Schechner, Y.Y.: Polarization: Beneficial for visibility enhancement? In: IEEE Conference on Computer Vision & Pattern Recognition (2009)
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)
Zhang, H., Sindagi, V., Patel, V.M.: Joint transmission map estimation and dehazing using deep networks. arXiv preprint arXiv:1708.00581 (2017)
Zhang, X., Dong, H., Hu, Z., Lai, W., Wang, F., Yang, M.: Gated fusion network for joint image deblurring and super-resolution. CoRR abs/1807.10806 (2018). http://arxiv.org/abs/1807.10806
Zhang, Y., Li, D., Sharma, G.: HazeRD: an outdoor scene dataset and benchmark for single image dehazing. In: IEEE International Conference on Image Processing (2018)
Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2472–2481 (2018)
Acknowledgments
This work was supported in part by the National Major Science and Technology Projects of China grant under number 2018ZX01008103, National Natural Science Foundation of China (61603291), Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2018JM6057) and the Fundamental Research Funds for the Central Universities.
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Xiang, L., Dong, H., Wang, F., Guo, Y., Ma, K. (2019). Gated Contiguous Memory U-Net for Single Image Dehazing. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11954. Springer, Cham. https://doi.org/10.1007/978-3-030-36711-4_11
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