Abstract
Remote sensing images are seriously degraded by multiple scattering and bad weather. Through the analysis of the radiative transfer procedure in atmosphere, an image atmospheric degradation model considering the influence of atmospheric absorption multiple scattering and non-uniform distribution is proposed in this paper. Based on the proposed model, a novel recovering method is presented to eliminate atmospheric degradation. Mean-shift image segmentation and block-wise deconvolution are used to reduce time cost, retaining a good result. The recovering results indicate that the proposed method can significantly remove atmospheric degradation and effectively improve contrast compared with other removal methods. The results also illustrate that our method is suitable for various degraded remote sensing, including images with large field of view (FOV), images taken in side-glance situations, image degraded by atmospheric non-uniform distribution and images with various forms of clouds.
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References
Tao, S., Feng, H., Xu, Z., Li, Q.: Image degradation and recovery based on multiple scattering in remote sensing and bad weather condition. Opt. Express 20, 16584–16595 (2012)
Rahman, Z., Jobson, D.J., Woodell, G.A.: “Retinex Processing for Automatic Image Enhancement,” In Human vision and electronic imaging VII, (International Society for Optics and Photonics, 2002), p. 12
Jobson, D.J., Rahman, Z., Woodell, G.: Properties and performance of a center/surround retinex. IEEE Trans. Image Process. 6, 451–462 (1997)
Tan, R.T.: In visibility in bad weather from a single image, 2008-01-01, IEEE 2008, pp. 1–8 (2008)
Fattal, R.: In single image dehazing, 2008-01-01, ACM, p. 72 (2008)
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33, 2341–2353 (2011)
Luzón-González, R., Nieves, J.L., Romero, J.: Recovering of weather degraded images based on RGB response ratio constancy. Appl. Optics 54, B222–B231 (2015)
Liu, J., Wang, X., Chen, M., Liu, S., Zhou, X., Shao, Z., Liu, P.: Thin cloud removal from single satellite images. Opt. Express 22, 618–632 (2014)
Narasimhan, S.G., Nayar, S.K.: In shedding light on the weather, 2003-01-01, IEEE 2003, p. 665 (2003)
He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1397–1409 (2013)
Matari, S., Deschenes, F.: In A New convolution kernel for atmospheric point spread function applied to computer vision, computer vision, 2007. ICCV 2007. IEEE 11th International Conference on, 2007-01-01, pp. 1–8 (2007)
Gonzalez, R.C., Woods R.E.: Digital image processing. Publishing House of Electronics Industry, Beijing (2010)
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24, 603–619 (2002)
Christoudias, C.M., Georgescu, B., Peter, M.: In Synergism in low level vision, Pattern Recognition, 2002. Proceedings. 16th International Conference on, 2002-01-01, pp. 150–155, (2002)
Meer, P., Georgescu, B.: “Edge detection with embedded confidence. IEEE Trans. Pattern Anal. Mach. Intell. 23, 1351–1365 (2001)
Fattal, R.: Dehazing using color-lines. ACM Trans. Gr. (TOG) 34, 11–13 (2014)
Gibson, K.B., Nguyen, T.Q.: In Fast single image fog removal using the adaptive wiener filter, image processing (ICIP), 2013 20th IEEE International Conference on, 2013-01-01, 2013, pp. 714–718, (2013)
Chen, Y., Xu, Z., Feng, H., Li, Q.: Image stabilization with support vector machine. J. Zhejiang Univ. Sci. C 12, 478–485 (2011)
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We thank the reviewers for helping us to improve this paper. The research work was supported by National Natural Science Foundation of China under Grant No. 61275021 and No. 61550003.
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Lin, G., Feng, H., Xu, Z. et al. Recovering of images degraded by atmosphere. Opt Rev 24, 471–482 (2017). https://doi.org/10.1007/s10043-017-0333-z
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DOI: https://doi.org/10.1007/s10043-017-0333-z