Abstract
In this paper, a fast enhancement method based on de-hazing is proposed for single low-light images. Instead of dark channel prior (DCP) used in the de-hazing related literature, the luminance map is used to estimate the global atmospheric light and the transmittance according to the observed similarity between the luminance map and DCP. Through this substitution, on the one hand the computation complexity is greatly reduced; on the other hand the block artifacts is also avoided brought by discontinuous transmittance estimated from DCP. Experimental results indicate that the proposed method has a significant improvement in both enhancement effects and processing speed compared with state-of-art enhancement algorithms.
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Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., Romeny, B.H., Zimmerman, J.B., Zuiderveld, K.: Adaptive histogram equalization and its variations. Comput. Vision Graph. Image Process. 39(3), 355–368 (1987)
Rahman, Z., Jobson, D.J., Woodell, G.: Multi-scale retinex for color image enhancement. In: Proceedings of the Third IEEE International Conference on Image Processing, Lausanne, Switzerland, vol. 3, pp. 1003–1006, 16–19 September 1996
Malm, H., Oskarsson, M., Warrant, E., Clarberg, P., Hasselgren, J., Lejdfors, C.: Adaptive enhancement and noise reduction in very low light-level video. In: Proceedings of the 11st IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil, pp. 1–8, 14–21 October 2007
Fu, H., Ma, H., Wu, S.: Night removal by color estimation and sparse representation. In: Proceedings of the 21st IEEE International Conference on Pattern Recognition, Tsukuba, Japan, pp. 3656–3659, 11–15 November 2012
Fotiadou, K., Tsagkatakis, G., Tsakalides, P.: Low light image enhancement via sparse representations. In: Campilho, A., Kamel, M. (eds.) ICIAR 2014. LNCS, vol. 8814, pp. 84–93. Springer, Heidelberg (2014). doi:10.1007/978-3-319-11758-4_10
Dong, X., Wang, G., Pang, Y., Li, W., Wen, J., Meng, W., Lu, Y.: Fast efficient algorithm for enhancement of low lighting video. In: Proceedings of the IEEE International Conference on Multimedia and Expo, Barcelona, Spain, pp. 1–6, 11–15 July 2011
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)
Tan, R.: Visibility in bad weather from a single image. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8, June 2008
Guo, F., Cai, Z., Xie, B., Tang, J.: Automatic image haze removal based on luminance component. In: Proceedings of the Sixth IEEE International Conference on Wireless Communications Networking and Mobile Computing, Chengdu, China, pp. 1–4, 23–25 September 2010
Zhang, X., Shen, P., Luo, L., Zhang, L., Song, J.: Enhancement and noise reduction of very low light level images. In: Proceedings of the 21st IEEE International Conference on Pattern Recognition, Tsukuba, Japan, pp. 2034–2037, 11–15 November 2012
Sen, P., Kalantari, N.K., Yaesoubi, M.: Robust patch-based HDR reconstruction of dynamic scenes. ACM Trans. Graph. 31(6), 439–445 (2012)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Acknowledgment
This project is supported by NSFC Grant (No. 61401324, No. 61305109, No. 61072105), by 863 Program (2013AA014601), and by Shaanxi Scientific research plan (2014K07-11, 2013K06-09).
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Song, J., Zhang, L., Shen, P., Peng, X., Zhu, G. (2016). Single Low-Light Image Enhancement Using Luminance Map. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 663. Springer, Singapore. https://doi.org/10.1007/978-981-10-3005-5_9
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DOI: https://doi.org/10.1007/978-981-10-3005-5_9
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