Abstract
The presence of haze shifts the color and degrades the visibility of outdoor scenes in digital images. In this paper, we propose a novel and effective optimization algorithm for single image dehazing. We first formulate the dehazing model into a linear convex optimization problem, and we develop its cost function based on two basic observations: first, a hazy image exhibits low contrast in general; second, the distance-map from the scene to the camera, is piecewise smooth. Then, we implement specific algorithm for our optimization problem using Split Bregman iteration. The experimental results show that our proposed algorithm not only enhances the contrast but also preserves the details and sharp edges. Our results demonstrate the effectiveness of the proposed optimization algorithm for dehazing.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Shwartz, S., Namer, E., Schechner, Y.Y.: Blind haze separation. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1984–1991 (2006)
Schechner, Y.Y., Averbuch, Y.: Regularized image recovery in scattering media. IEEE Transactions on Pattern Analysis & Machine Intelligence 29(9), 1655–1660 (2007)
Kopf, J., Neubert, B., Chen, B., Cohen, M., Cohen-Or, D., Deussen, O., Uyttendaele, M., Lischinski, D.: Deep photo: Model-based photograph enhancement and viewing. In: SIGGRAPH Asia (2008)
Hautiere, N., Tarel, J., Aubert, D.: Toward fog-free in-vehicle vision systems through contrast restoration. In: IEEE Conference on Computer Vision and Pattern Recognition (2007)
Robby, T.: Tan: Visibility in bad weather from a single image. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, United States, pp. 1–8 (2008)
Fattal, R.: Single image dehazing. In: SIGGRAPH, New York, USA, pp. 1–8 (2008)
Tarel, J.-P., Hautiere, N.: Fast visibility resortation from a single color or gray level image. In: IEEE 12th International Conference on Computer Vision, Kyoto, Japan, pp. 2201–2208 (2009)
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1956–1963 (2009)
He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (2013)
Anitharani, M., Padma, S.I.: Literature survey of haze removal of secure remote surveillance system. International Journal of Engineering Research & Technology (IJERT)Â 2 (January 2013)
Ancuti, C.O., Ancuti, C.: Single image dehazing by multi-scale fusion. IEEE Transactions on Image Processing 22(8) (August 2013)
Chiang, J.Y., Chen, Y.-C.: Underwater image enhancement by wavelength compensation and dehazing. IEEE Transactions on Image Processing 21(4) (April 2012)
Kim, J.-H., Jang, W.-D., Sim, J.-Y., Kim, C.-S.: Optimized contrast enhancement for real-time image and video dehazing. Journal of Visual Communication and Image Representation 24(3), 410–425 (2013)
Yang, Z., Zhang, C., Xie, L.: Robustly stable signal recovery in compressed sensing with structured matrix perturbation. IEEE Transactions on Signal Processing 60(9) (September 2012)
Yang, Z., Zhang, C., Lu, W.: Orthonormal expansion l 1-minimization algorithms for compressed sensing. IEEE Transactions on signal processing 59(12) (December 2011)
Grant, M., Boyd, S.: CVX: Matlab software for disciplined convex programming (2008), http://cvrx.com/cvx
Goldstein, T., Osher, S.: The split Bregman method for l 1 regularized problems, ftp://ftp.math.ucla.edu/pub/camreport/cam08-29.pdf
Chiang, J.Y., Chen, Y.-C.: Underwater image enhancement by wavelength compensation and dehazing. IEEE Transactions on Image Processing 21(4) (April 2012)
Caraffa, L., Tarel, J.-P.: Markov random field model for single image dehazing. In: IEEE Intelligent Vehicle Symposium, pp. 994–999 (June 2013)
Guo, F., Tang, J., Peng, H.: A Markov random field model for the restoration of foggy images. International Journal of Advanced Robotic Systems (June 2014)
Gao, Y., Hu, H., Wang, S., Li, B.: A fast image dehazing algorithm based on negative correction. International Journal of Signal Processing 103, 380–398 (2014)
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica, 259–268 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
He, J., Zhang, C., Baqee, IA. (2014). Image Dehazing Using Regularized Optimization. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8887. Springer, Cham. https://doi.org/10.1007/978-3-319-14249-4_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-14249-4_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14248-7
Online ISBN: 978-3-319-14249-4
eBook Packages: Computer ScienceComputer Science (R0)