Abstract
In order to improve the pilots’ perception of the runway and the surrounding things in foggy days and improve the visual effect of aerial images, a combination of Gamma correction and Retinex de-fogging algorithm is proposed for aerial foggy images. First of all, the original image is corrected by Gamma as the guided map, and the light intensity of the aerial image is estimated by the guided filter, and the preliminary fog removal image is obtained by Retinex. In combination with the histogram truncation technique, the output of the image is mapped to between 0 and 255, then a de-fogging enhanced image is achieved. Compared with other de-fogging algorithms, this algorithm has higher contrast and color consistency.
Supported in part by Sichuan Science and Technology Program under Grant No. 2019YJ0541, the Open Project of Sichuan Province University Key Laboratory of Bridge Non-destruction Detecting and Engineering Computing under Grant No. 2019QZJ03 and Natural Science Foundation of Sichuan University of Science and Engineering (SUSE) under Grant No. 2019RC09, 2020RC28.
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References
Fattal, R.: Single image dehazing. ACM Trans. Graph. (TOG) 27(3), 72 (2008)
He, K., Sun, J., Tang, X.: Guided image filtering. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 1–14. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15549-9_1
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2010)
He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2012)
Huang, D., Fang, Z., Zhao, L., Chu, X.: An improved image clearness algorithm based on dark channel prior. In: Proceedings of the 33rd Chinese Control Conference, pp. 7350–7355. IEEE (2014)
Jobson, D.J., Rahman, Z.U., Woodell, G.A.: Properties and performance of a center/surround retinex. IEEE Trans. Image Process. 6(3), 451–462 (1997)
Kapoor, R., Gupta, R., Son, L.H., Kumar, R., Jha, S.: Fog removal in images using improved dark channel prior and contrast limited adaptive histogram equalization. Multimed. Tools Appl. 78(16), 23281–23307 (2019). https://doi.org/10.1007/s11042-019-7574-8
Land, E.H., McCann, J.: Lightness and retinex theory. J. Opt. Soc. Am. 61(1), 1–11 (1971)
Li, S., Ren, W., Zhang, J., Yu, J., Guo, X.: Single image rain removal via a deep decomposition–composition network. In: Computer Vision and Image Understanding (2019)
Liu, C., Cheng, I., Zhang, Y., Basu, A.: Enhancement of low visibility aerial images using histogram truncation and an explicit retinex representation for balancing contrast and color consistency. ISPRS J. Photogram. Remote Sens. 128, 16–26 (2017)
Liu, P., Wang, M., Wang, L., Han, W.: Remote-sensing image denoising with multi-sourced information. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 12(2), 660–674 (2019)
Livingston, M.A., Garrett, C.R., Ai, Z.: Image processing for human understanding in low-visibility. Techical report, Naval Research Lab Washington DC Information Technology Div (2011)
Lu, H., Li, Y., Nakashima, S., Serikawa, S.: Single image dehazing through improved atmospheric light estimation. Multimed. Tools Appl. 75(24), 17081–17096 (2015). https://doi.org/10.1007/s11042-015-2977-7
Patil, M.D., Sutar, M.S., Mulla, M.A.: Automatic image enhancement for better visualization using retinex technique. Int. J. Sci. Res. Publ. 3(6), 1–4 (2013)
Pu, Y.F., et al.: A fractional-order variational framework for retinex: fractional-order partial differential equation-based formulation for multi-scale nonlocal contrast enhancement with texture preserving. IEEE Trans. Image Process. 27(3), 1214–1229 (2017)
Sahu, S., Singh, A.K., Ghrera, S., Elhoseny, M., et al.: An approach for de-noising and contrast enhancement of retinal fundus image using clahe. Optics Laser Technol. 110, 87–98 (2019)
Suarez, P.L., Sappa, A.D., Vintimilla, B.X., Hammoud, R.I.: Deep learning based single image dehazing. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2018)
Tanchenko, A.: Visual-psnr measure of image quality. J. Vis. Commun. Image Represent. 25(5), 874–878 (2014)
Wang, W., Yuan, X.: Recent advances in image dehazing. IEEE/CAA J. Automatica Sinica 4(3), 410–436 (2017)
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Liu, X., Liu, C., Lan, H. (2020). Fog Removal of Aerial Image Based on Gamma Correction and Guided Filtering. In: McDaniel, T., Berretti, S., Curcio, I., Basu, A. (eds) Smart Multimedia. ICSM 2019. Lecture Notes in Computer Science(), vol 12015. Springer, Cham. https://doi.org/10.1007/978-3-030-54407-2_40
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