Abstract
In order to dehaze the outdoor hazy images with different fog levels faster and more accurately, a method based on convolutional neural network (CNN) is proposed, called End-to-End Pyramid Dehazing Network (EPD-Net). EPD-Net is a light-weight CNN with three modules: the T-estimation module, coarse dehazing module and pyramid pooling module. A depth estimation method based on CNN and a sky segmentation algorithm are used to estimate and modify the depth maps of outdoor image datasets, which aimed to synthesize hazy images of different fog levels. Experimental results demonstrate that on both the synthesized and the natural hazy image datasets, the proposed EPD-Net achieve superior dehazing performance than other representative dehazing algorithms in terms of objective indicators such as PSNR, SSIM, running time and the subjective visual quality.
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References
Jobson, D.J., Woodell, G.A.: Retinex processing for automatic image enhancement. Electron. Imaging 13(1), 100–110 (2004)
He, K.M., Sun, J.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Intell. 33(12), 2341–2353 (2011)
Tang, J., Chen, Z., Su, B., Zheng, J.: Single image defogging based on step estimation of transmissivity. In: Wang, Y., Wang, S., Liu, Y., Yang, J., Yuan, X., He, R., Duh, H.B.-L. (eds.) IGTA 2017. CCIS, vol. 757, pp. 74–84. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-7389-2_8
Sulami, M., Glatzer, I., Fattal, R.: Automatic recovery of the atmospheric light in hazy images. In: IEEE International Conference on Computational Photography (ICCP), pp. 1–11. IEEE (2017)
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
Cai, B., Xu, X., Jia, K.: Dehazenet: an end-to-end system for single image haze removal. IEEE Trans. Image Process. 25(11), 5187–5198 (2016)
Li, B., Peng, X., Wang, Z.: AOD-net: al-in-one dehazing network. In: IEEE International Conference on Computer Vision, Venice, Italy, 22–29 October, pp. 4770–4778 (2017)
Zhang, H., Patel, V.M.: Densely connected pyramid dehazing network. In: The 32th IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 19–21 June, pp. 2261–2269 (2018)
Li, B., Ren, W., Fu, D., et al.: Benchmarking single-image dehazing and beyond. IEEE Trans. Image Process. 28(1), 492–505 (2019)
Liu, F., Shen, C., Lin, G., et al.: Learning depth from single monocular images using deep convolutional neural fields. IEEE Trans. Pattern Anal. Mach. Intell. 10, 2024–2039 (2016)
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Yang, C., Liu, Z., Liu, S., Qin, J., Chen, D. (2019). An End-to-End Pyramid Convolutional Neural Network for Dehazing. In: Wang, Y., Huang, Q., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2019. Communications in Computer and Information Science, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-13-9917-6_5
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DOI: https://doi.org/10.1007/978-981-13-9917-6_5
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