Single image dehazing through improved atmospheric light estimation
- 787 Downloads
Image contrast enhancement for outdoor vision is important for smart car auxiliary transport systems. The video frames captured in poor weather conditions are often characterized by poor visibility. Most image dehazing algorithms consider to use a hard threshold assumptions or user input to estimate atmospheric light. However, the brightest pixels sometimes are objects such as car lights or streetlights, especially for smart car auxiliary transport systems. Simply using a hard threshold may cause a wrong estimation. In this paper, we propose a single optimized image dehazing method that estimates atmospheric light efficiently and removes haze through the estimation of a semi-globally adaptive filter. The enhanced images are characterized with little noise and good exposure in dark regions. The textures and edges of the processed images are also enhanced significantly.
KeywordsImage dehazing Image restoration Image enhancement Atmospheric light estimation
All of the authors have the same contribution to this paper. This work was supported by Grant in Aid for Foreigner Research Fellows of Japan Society for the Promotion of Science (No.15F15077), Open Fund of the Key Laboratory of Marine Geology and Environment in Chinese Academy of Sciences (No.MGE2015KG02), Research Fund of State Key Laboratory of Marine Geology in Tongji University (MGK1407), Research Fund of State Key Laboratory of Ocean Engineering in Shanghai Jiaotong University (OEK1315), and Grant in Aid for Research Fellows of Japan Society for the Promotion of Science (No.13 J10713).
- 2.C. Ancuti, C. O. Ancuti, T. Haber, P. Bekaert, “Enhancing underwater images and videos by fusion”, in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 81–88, 2012.Google Scholar
- 3.F. Cozman, E. Krotkov, “Depth from scattering”, in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp.801–806, 1997.Google Scholar
- 5.Fattal R (2014) Dehazing using color-lines. ACM Trans Graph:1–10Google Scholar
- 7.N. Hautiere, J.P. Tarel, D. Aubert, “Towards fog-free in-vehicle vision systems through contrast restoration”, in IEEE Conference on Computer Vision and Pattern Recognition, pp.1–8, 2008.Google Scholar
- 11.Koschmieder H (1925) Theorie der horizontalen sichtweite: kontrast und sichtweite. Keim Nemnich Press 2:1–11Google Scholar
- 16.Schechner Y, Narasimhan S, Nayar S (2001) “Instant dehazing of images using polarization”, in: proc. of computer vision and. Pattern Recogn 1:325–332Google Scholar
- 17.Schettini R, Corchs S (2010) Underwater image processing: state of the art of restoration and image enhancement methods. EURASIP J Adv Signal Process 746052Google Scholar
- 19.R.T. Tan, “Visibility in bad weather from a single image”, in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp.1–8, 2008.Google Scholar
- 20.J. Tarel, N. Hautiere, “Fast visibility restoration from a single color or gray level image”, in Proc. of the IEEE International Conference on Computer Vision, pp. 2201–2208, 2009.Google Scholar
- 22.S. Yao, W. Lin, E. Ong, Z. Lu, “Contrast signal to noise ratio for image quality assessment”, in Proc. of International Conference on Image Processing, pp. 397–400, 2005.Google Scholar