Abstract
Fog is an atmospheric phenomenon that significantly degrades the visibility of outdoor scenes. Thus, this paper presents an algorithm to remove fog for a single image. The method estimates the transmission map of image degradation model by assigning labels with MRF model and optimizes the map estimation process using the graph-cut based α-expansion technique. The algorithm goes with two steps: first, the transmission map is estimated using a dedicated MRF model combined with the bilateral filter. Then, the restored image is obtained by taking the estimated transmission map and the airlight into the image degradation model to recover the scene radiance. A comparative study is proposed with a few other state of the art algorithms which demonstrate that better quality results can be obtained using the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Li, S.Z.: Markov random field modeling in image analysis, p. 21. Springer-Verlag London Limited, UK (2009)
Nishino, K., Kratz, L., Lmbardi, S.: Bayesian defogging. International Journal of Computer Vision 98(3), 263–278 (2012)
Kratz, L., Nishino, K.: Factorizing scene albedo and depth from a single foggy image. In: IEEE International Conference on Computer Vision (ICCV), pp. 1701–1708 (2009)
Fattal, R.: Single image dehazing. ACM Transactions on Graphics 27(3), 1–9 (2008)
Tan, R.T.: Visibility in bad weather from a single image. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2008)
Carr, P., Hartley, R.: Improved single image dehazing using geometry. In: The Digital Image Computing: Technique and Applications, Melbourne, pp. 103–110 (2009)
Cataffa, L., Tarel, J.P.: Markov random field model for single image defogging. In: IEEE Intelligent Vehicle Symposium, pp. 994–999 (2013)
Caraffa, L., Tarel, J.-P.: Stereo reconstruction and contrast restoration in daytime fog. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part IV. LNCS, vol. 7727, pp. 13–25. Springer, Heidelberg (2013)
Wang, Y.K., Fan, C.T., Chang, C.W.: Accurate depth estimation for image defogging using Markov Random Field. In: International Conference on Graphic and Image Processing (ICGIP), Singapore, pp. 1–5 (2012)
Kolmogorov, V., Zabin, R.: What energy function can be minimized via graph cuts? IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 26(2), 147–159 (2004)
Boykoc, Y., Kolmogorov, V.: An experimental comparison of Min-cut/Max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 26(9), 1124–1137 (2004)
Rossum, M.V., Nieuwenhuizen, T.: Multiple scattering of classical waves: microscopy, mesoscopy and diffusion. Reviews of Modern Physics 71(1), 313–371 (1999)
Narasimhan, S.G., Nayar, S.K.: Vision and the atmosphere. International Journal on Computer Vision 48(3), 233–254 (2002)
The gco-v3.0 library (gco-v3.0), http://vision.csd.uwo.ca/code/ (accessed April 5, 2013)
Boykov, Y., Veksler, O., Zabin, R.: Fast approximate energy minimization via graph cuts. IEEE Transaction on Pattern Analysis and Machine Intelligence (PAMI) 23(11), 1222–1239 (2001)
Delong, A., Osokin, A., Isack, H.N., Boykov, Y.: Fast Approximate Energy Minimization with Label Costs. International Journal of Computer Vision 96(1), 1–27 (2012)
Fattal, R.: Single image dehazing, ACM Transactions on Graphics (SIGGRAPH 2008) 27, 72:1–72:9 (2008)
Tan, R.T.: Visibility in bad weather from a single image. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), United States, pp. 1–8 (2008)
He, K.M., Sun, J., Tang, X.O.: Single image haze removal using dark channel prior. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(12), 2341–2353 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guo, F., Peng, H., Tang, J. (2014). A New Restoration Algorithm for Single Image Defogging. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-662-45643-9_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45642-2
Online ISBN: 978-3-662-45643-9
eBook Packages: Computer ScienceComputer Science (R0)