Abstract
The visual quality of the images gets decreased due to bad weather conditions. The image captured under hazy weather conditions have serious attenuation in terms of color and saturation. In addition, these hazy images have very low contrast and the visual quality will be drastically poor. Moreover, object detection in hazy environment is too challenging. So, single image dehazing is a demanding, challenging and ill-posed problem. In this paper, we propose a 9-layer convolutional neural network with deep dilated filters of different dilation rates to achieve an end-to-end mapping from haze image to haze free image. Exponential expansion of receptive field is possible with the dilated filters without increasing the model complexity. Furthermore, the dilated convolutional layers help for efficient model compactness. We did experiments on synthetic dataset and on naturally obtained hazy images. The results show that our network achieves outstanding performance over the existing algorithms in terms of PSNR, SSIM and visual quality.
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References
Woodell, G., Jobson, D.J., Rahman, Z., Hines, G.: Advanced image processing of aerial imagery. In: Proceedings of the SPIE 6246, Visual Information Processing XV, 62460E, 12 May 2006
Shao, L., Liu, L., Li, X.: Feature learning for image classification via multiobjective genetic programming. IEEE Trans. Neural Networks Learn. Syst. 25(7), 1359–1371 (2014). https://doi.org/10.1109/TNNLS.2013.2293418
Liu, Q., Gao, X.,He, L., Lu, W.: Haze removal for a single visible remote sensing image. Signal Processing 137, 3343 (2017). ISSN 0165-1684, https://doi.org/10.1016/j.sigpro.2017.01036
Kim, T.K., Paik, J.K., Kang, B.S.: Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering. IEEE Trans. Consumer Electron. 44(1), 82–87 (1998). https://doi.org/10.1109/30.663733
Stark, J.A.: Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Trans. Image Process. 9(5), 889–896 (2000). https://doi.org/10.1109/83.841534
Schechner, Y., Narasimhan, S., Nayar, S.: Polarization-based vision through haze. Appl. Opt. 42, 511–525 (2003)
Narasimhan, S.G., Nayar, S.K.: Contrast restoration of weather degraded images. IEEE Trans. Pattern Analysis and Machine Intell. 25(6), 713–724 (2003). https://doi.org/10.1109/TPAMI.2003.1201821
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). https://doi.org/10.1109/TIP.2016.2598681
Galdran, A.: Image dehazing by artificial multiple-exposure image fusion. Signal Process. 149, 135–147 (2018). ISSN 0165-1684, https://doi.org/10.1016/j.sigpro.2018.03.008
Cantor, A.: Optics of the atmosphere–scattering by molecules and particles. IEEE J. Quant. Electron. 14(9), 698–699 (1978). https://doi.org/10.1109/JQE.1978.1069864
Li, B., et al.: Benchmarking single-image dehazing and beyond. IEEE Trans. Image Process. 28(1), 492–505 (2019). https://doi.org/10.1109/TIP.2018.2867951
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2014)
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Deivalakshmi, S., Sudaroli Sandana, J. (2023). Deep Dilated Convolutional Network for Single Image Dehazing. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_22
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