In order to improve the visual effect of aerial images in foggy weather, a Retinex defogging algorithm combining dark channel priors is proposed to solve the phenomenon of insufficient defogging and over-enhancement of aerial defogging in Reinex defogging. First, the foggy original image is decomposed into foggy incident light components and foggy reflected light components by Retinex theory. Then, the principle of foggy image degradation is analyzed by an atmospheric scattering model, and the dark channel prior defogging algorithm is used to obtain the incident light component and reflected light component after defogging. Finally, a fog-free image was restored through the Reitnex model. The effectiveness of this algorithm is verified through experiments. By comparing and analyzing this algorithm with other defogging algorithms, this algorithm has higher contrast and color fidelity.
Cite this paper
Liu, X. , Liu, C. , Lan, H. and Xie, L. (2020). Dehaze Enhancement Algorithm Based on Retinex Theory for Aerial Images Combined with Dark Channel. Open Access Library Journal, 7, e6280. doi: http://dx.doi.org/10.4236/oalib.1106280.
Li, S., Ren, W., Zhang, J., et al. (2019) Single Image Rain Removal via a Deep Decomposition-Composition Network. Computer Vision and Image Understanding.
https://doi.org/10.1016/j.cviu.2019.05.003
He, K.M., Sun, J. and Tang, X. (2011) Single Image Haze Removal Using Dark Channel Prior. IEEE Transcations on Pattern Analysis & Machine Intelligence, 33, 2341-2353. https://doi.org/10.1109/TPAMI.2010.168
Huang, D.R., et al. (2014) An Improved Image Clearness Algorithm Based on Dark Channel Prior. Proceedings of the 33rd Chinese Control Conference, Nanjing, 28-30 July 2014. https://doi.org/10.1109/ChiCC.2014.6896219
Rajiv, K., et al. (2019) Fog Removal in Images Using Improved Dark Channel Prior and Contrast Limited Adaptive Histogram Equalization. Multimedia Tools and Applications, 78, 23281-23307. https://doi.org/10.1007/s11042-019-7574-8
Sonali, S.S., Singh, A.K., Ghrera, S.P. and Elhoseny, M. (2018) An Approach for De-Noising and Contrast Enhancement of Retinal Fundus Image Using CLAHE. Optics and Laser Technology, 110, 87-98.
https://doi.org/10.1016/j.optlastec.2018.06.061
Jobson, D.J., Rahman, Z.-U., Woodell, G., et al. (1997) Properties and Performance of a Center/Surround Retinex. IEEE Transactions on Image Processing, 6, 451-462.
https://doi.org/10.1109/83.557356
Patil, M.D.V., Sutar, M.S.G. and Mulla, M.A.N. (2013) Automatic Image Enhancement for Better Visualization Using Retinex Technique. International Journal of Scientific and Research Publications, 3, No. 6.
Livingston, M.A., Garrett, C.R. and Ai, Z. (2011) Image Processing for Human Understanding in Low-Visibility. Naval Research Lab Information Technology DIV, Washington DC. https://doi.org/10.21236/ADA609988
Liu, C.J., Cheng, I., Zhang, Y. and Basu, A. (2017) Enhancement of Low Visibility Aerial Images Using Histogram Truncation and an Explicit Retinex Representation for Balancing Contrast and Color Consistency. Journal of Photogrammetry and Remote Sensing, 128, 16-26. https://doi.org/10.1016/j.isprsjprs.2017.02.016
Suárez, P.L., et al. (2018) Deep Learning Based Single Image Dehazing. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, 18-22 June 2018. https://doi.org/10.1109/CVPRW.2018.00162
Tarel, J.P. and Nicolas, H. (2009) Fast Visibility Restoration from a Single Color or Gray Level Image. IEEE 12th International Conference on Computer Vision, ICCV 2009, Kyoto, Japan, 27 September- 4 October 2009.
https://doi.org/10.1109/ICCV.2009.5459251
