Abstract
The dark channel prior defogging algorithm is one of the most representative image defogging algorithms. However, the restored images have problems such as halo effect, color distortion and so on. In view of the above problems, we proposes a method based on the red channel prior algorithm to estimate the transmission maps of different color channels. After defogging, it will reduce the brightness and contrast of the image, resulting in the loss of detail information. Therefore, this paper uses gamma function on the defogged image to performs color compensation and optimazes the detailed information of the image. The algorithm proposed in this article is used in the contrast experiment with other defogging algorithms, and the experimental results are evaluated objectively based on the information entropy and other parameters. Through experimental comparison, our algorithm compared to the dark channel prior defogging algorithm to obtain a clearer image defogging image, and the subjective vision is more consistent with the real scene.
Supported by: National Natural Science Foundation of China (61862029) and Shanghai Normal University (Research on Automatic Focus Algorithm (209-AC9103-20-368005221)). The corresponding authors: Zhang Xiangfen and Yuan Feiniu.
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References
Zhou, S.L., Zou, H.X., Xing, X.W., Ji, K.F., Leng, X.G.: Hybrid bilateral filtering algorithm based on edge detection. IET Image Process. 10(11), 809–816 (2016)
Qiao, T., Ren, J.C., Wang, Z., Zabalza, J.: Effective denoising and classification of hyperspectral images using curvelet transform and singular spectrum analysis. IEEE Trans. Geosci. Remote Sens. 55(1), 119–133 (2017)
Li, Y., et al.: Fast morphological filtering haze removal method from a single image. J. Comput. Inf. Syst. 11(16), 5799–5806 (2015)
Tian, H.Y., Xue, M., Ming, Z., Meng, H.: An improved multi-scale retinex fog and haze image enhancement method. In: Proceedings of the 2016 International Conference on Information System and Artificial Intelligence, Piscataway, pp. 557–560. IEEE (2017)
He, K.M., Sun, J., Yang, X.O.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)
Liu, Y., Zhang, H., Wu, Y., et al.: A fast single image defogging algorithm based on semi-inverse method. J. Graph. 36(1), 68–76 (2015)
Xiao, J., Peng, H., Zhang, Y., et al.: Fast image enhancement based on color space fusion. Color Res. Appl. 41(1), 22–31 (2014)
Garldran, A., Pardo, D., Picon, A., Alvarez-Gila, A.: Automatic red-channel underwater images restoration. J. Vis. Commun. Image Represent. 26, 132–145 (2015)
Zhao, X.W., Jin, T., Qu, S.: Deriving inherent optical properties from background color and underwater image enhancement. Ocean Eng. 94, 163–172 (2015)
Gould, R.W., Arnone, R.A., Martinolich, P.M.: Spectral dependence of the scattering coefficient in case 1 and case 2 waters. Appl. Opt. 38(12), 2377–2383 (1999)
Fergus, K.: Dark flash photography. ACM SIGGRAPH 28(3) (2009)
Huang, S.C., Cheng, F.C., Chiu, Y.S.: Efficient contrast enhancement using adaptive gamma correction with weighting distribution. IEEE Trans. Image Process. Publ. IEEE Signal Process. Soc. 22(3), 1032–1041 (2013)
Wang, S., Rehman, A., Zhou, W., et al.: SSIM-motivated rate-distortion optimization for video coding. IEEE Trans. Circ. Syst. Video Technol. 22(4), 516–529 (2012)
Hao, C., Jian, C., Qingzhou, Y.E., et al.: Autofocus algorithm based on adjacent pixel difference and NRSS. Comput. Eng. 41(9), 261–265 (2015)
Zhu, Q.S., Mai, J.M., Shao, L.: A fast single image haze removal algorithm using color attenuation prior. IEEE Trans. Image Process. 24(11) (2015). https://doi.org/10.1109/TIP.2015.2446191
Berman, D., Treibitz, T., Avidan, S.: Non-local image dehazing (2015)
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Zhang, Q., Zhao, C., Zhang, X., Yuan, F., Li, C., Hao, D. (2021). An Image Dehazing Algorithm Based on Adaptive Correction and Red Dark Channel Prior. In: Zhai, G., Zhou, J., Yang, H., An, P., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2020. Communications in Computer and Information Science, vol 1390. Springer, Singapore. https://doi.org/10.1007/978-981-16-1194-0_3
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