Abstract
The primary challenge for removing haze from a single image is lack of decomposition cues between the original light transport and airlight scattering in a scene. Many dehazing algorithms start from an assumption on natural image statistics to estimate airlight from sparse cues. The sparsely estimated airlight cues need to be propagated according to the local density of airlight in the form of a transmission map, which allows us to obtain a haze-free image by subtracting airlight from the hazy input. Traditional airlight-propagation methods rely on ordinary regularization on a grid random field, which often results in isolated haze artifacts when they fail in estimating local density of airlight properly. In this work, we propose a non-local regularization method for dehazing by combining Markov random fields (MRFs) with nearest-neighbor fields (NNFs) extracted from the hazy input using the PatchMatch algorithm. Our method starts from the insightful observation that the extracted NNFs can associate pixels at the similar depth. Since regional haze in the atmosphere is correlated with its depth, we can allow propagation across the iso-depth pixels with the MRF-based regularization problem with the NNFs. Our results validate how our method can restore a wide range of hazy images of natural landscape clearly without suffering from haze isolation artifacts. Also, our regularization method is directly applicable to various dehazing methods.
The initial version of this manuscript was published in conference proceedings of International Conference on Computer Vision, Theory and Applications (VISAPP 2017) [1]. This paper was invited to be published in the Communications in Computer and Information Science series by Springer afterward. It is a revised and extended version of the conference paper to additionally introduce more scientific analysis and evaluation on the proposed method.
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References
Kim, I., Kim, M.H.: Dehazing using non-local regularization with Iso-depth neighbor-fields. In: Proceedings of International Conference Computer Vision, Theory and Applications (VISAPP 2017), Porto, Portugal (2017)
He, K.M., Sun, J., Tang, X.: Single image haze removal using dark channel prior. In: Proceedings of IEEE CVPR, pp. 1956–1963 (2009)
Fattal, R.: Single image dehazing. ACM Trans. Graph. 27, 72:1–72:9 (2008)
Fattal, R.: Dehazing using color-lines. ACM Trans. Graph. 34, 13:1–13:14 (2014)
Berman, D., Treibitz, T., Avidan, S.: Non-local image dehazing. In: IEEE CVPR, pp. 1674–1682 (2016)
Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.B.: Patchmatch: a randomized correspondence algorithm for structural image editing. ACM Trans. Graph. 28, 24:1–24:11 (2009)
Narasimhan, S.G., Nayar, S.K.: Vision and the atmosphere. Int. J. Comput. Vis. 48, 233–254 (2002)
Schechner, Y.Y., Narasimhan, S.G., Nayar, S.K.: Instant dehazing of images using polarization. In: Proceedings of IEEE CVPR, pp. I:325–I:332 (2001)
Narasimhan, S.G., Nayar, S.K.: Contrast restoration of weather degraded images. IEEE Trans. Pattern Anal. Mach. Intell. 25, 713–724 (2003)
Kopf, J., et al.: Deep photo: model-based photograph enhancement and viewing. ACM Trans. Graph. 27, 116:1–116:10 (2008)
Tang, K., Yang, J., Wang, J.: Investigating haze-relevant features in a learning framework for image dehazing. In: Proceedings of IEEE CVPR, pp. 2995–3002 (2014)
Zhu, Q., Mai, J., Shao, L.: A fast single image haze removal algorithm using color attenuation prior. IEEE Trans. Image Process. 24, 3522–3533 (2015)
Tan, R.T.: Visibility in bad weather from a single image. In: Proceedings of IEEE CVPR, pp. 1–8 (2008)
Tarel, J., Hautière, N.: Fast visibility restoration from a single color or gray level image. In: Proceedings of IEEE ICCV, pp. 2201–2208 (2009)
Nishino, K., Kratz, L., Lombardi, S.: Bayesian defogging. Int. J. Comput. Vis. 98, 263–278 (2012)
Ancuti, C.O., Ancuti, C.: Single image dehazing by multi-scale fusion. IEEE Trans. Image Process. 22, 3271–3282 (2013)
Li, Y., Tan, R.T., Brown, M.S.: Nighttime haze removal with glow and multiple light colors. In: 2015 IEEE ICCV 2015, Santiago, Chile, 7–13 December 2015, pp. 226–234 (2015)
Meng, G., Wang, Y., Duan, J., Xiang, S., Pan, C.: Efficient image dehazing with boundary constraint and contextual regularization. In: Proceedings of IEEE ICCV, pp. 617–624 (2013)
Carr, P., Hartley, R.I.: Improved single image dehazing using geometry. In: DICTA 2009, pp. 103–110 (2009)
He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1397–1409 (2013)
Marroquín, J.L., Velasco, F.A., Rivera, M., Nakamura, M.: Gauss-Markov measure field models for low-level vision. IEEE Trans. Pattern Anal. Mach. Intell. 23, 337–348 (2001)
Li, Y.P., Huttenlocher, D.P.: Sparse long-range random field and its application to image denoising. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5304, pp. 344–357. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88690-7_26
Zhang, Q., Xu, L., Jia, J.: 100+ times faster weighted median filter (WMF). In: CVPR, pp. 2830–2837. IEEE (2014)
Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE Trans. Pattern Anal. Mach. Intell. 30, 228–242 (2008)
Kim, M.H., Kautz, J.: Consistent tone reproduction. In: Proceedings of the IASTED International Conference on Computer Graphics and Imaging (CGIM 2008), Innsbruck, Austria, pp. 152–159. IASTED/ACTA Press (2008)
Kim, M.H., Kautz, J.: Consistent scene illumination using a chromatic flash. In: Proceedings of Eurographics Workshop on Computational Aesthetics (CAe 2009), British Columbia, Canada, Eurographics, pp. 83–89 (2009)
Acknowledgments
Min H. Kim, the corresponding author, acknowledges Korea NRF grants (2016R1-A2B2013031, 2013M3A6A6073718) and additional support by KOCCA in MCST of Korea, Cross-Ministry Giga KOREA Project (GK17P0200), Samsung Electronics (SRFC-IT1402-02), and an ICT R&D program of MSIT/IITP of Korea (2017-0-00072, 2016-0-00018). We also would like to appreciate Seung-Hwan Baek’s helpful comments.
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Kim, I., Kim, M.H. (2019). Non-local Haze Propagation with an Iso-Depth Prior. In: Cláudio, A., et al. Computer Vision, Imaging and Computer Graphics – Theory and Applications. VISIGRAPP 2017. Communications in Computer and Information Science, vol 983. Springer, Cham. https://doi.org/10.1007/978-3-030-12209-6_11
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