Abstract
Variational Retinex model-based methods for low-light image enhancement have been popularly studied in recent years. In this paper, we present an enhanced variational Retinex method for low-light natural image enhancement, based on the initial smoother illumination component with a structure extraction technique. The Bergman splitting algorithm is then introduced to estimate the illuminance component and reflectance component. The de-block processing and illuminance component correction are used for the enhanced reflectance as the ultimate enhanced image. Moreover, the estimated smoother illumination component can make enhanced images preserve edge details. Experimental results with a comparison demonstrate the present variational Retinex method can effectively enhance image quality and maintain image color.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, Y., Chen, Q., Zhang, B.: Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Trans. Consum. Electron. 45(1), 1–8 (1999)
Kim, Y.T.: Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans. Consum. Electron. 43(1), 1–8 (1997)
Loza, A., Bull, D.R., Hill, P.R., Achim, A.M.: Automatic contrast enhancement of low-light images based on local statistics of wavelet coefficients. Digit. Sig. Process. 23(6), 1856–1866 (2013)
Kim, J.H., Kim, J.-H., Jung, S.W., Noh, C.K., Ko, S.J.: Novel contrast enhancement scheme for infrared image using detail-preserving stretching. Opt. Eng. 50(7), 1–11 (2011)
Jobson, D.J., Rahman, Z., Woodell, G.A.: Properties and performance of a center/surround retinex. IEEE Trans. Image Process. 6(3), 451–462 (1997). A Publication of the IEEE Signal Processing Society
Jobson, D.J., Rahman, Z., Woodell, G.A.: A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image Process. 6(7), 965–976 (1997)
Kimmel, R., Elad, M., Shaked, D., Keshet, R., Sobel, I.: A variational framework for retinex. Int. J. Comput. Vis. 52(1), 7–23 (2003)
Wu, X.: A linear programming approach for optimal contrast-tone mapping. IEEE Trans. Image Process. 20(5), 1262–1272 (2011)
Huang, S.C., Cheng, F.C., Chiu, Y.S.: Efficient contrast enhancement using adaptive gamma correction with weighting distribution. IEEE Trans. Image Process. 22(3), 1032–1041 (2013)
Fu, X., Liao, Y., Zeng, D., Huang, Y., Zhang, X., Ding, X.: A probabilistic method for image enhancement with simultaneous illumination and reflectance estimation. IEEE Trans. Image Process. 24(12), 4965–4977 (2015)
Fu, X., Sun, Y., LiWang, M., Huang, Y., Zhang, X.P., Ding, X.: A novel Retinex based approach for image enhancement with illumination adjustment. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, pp. 1190–1194 (2014). https://doi.org/10.1109/ICASSP.2014.6853785
Park, S., Moon, B., Ko, S., Yu, S., Paik, J.: Low-light image enhancement using variational optimization-based Retinex model. In: IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, pp. 70–71 (2017). https://doi.org/10.1109/ICCE.2017.7889233
Guo, X.: Lime: a method for low-light image enhancement. In: Proceedings of MM International Multimedia Conference 2016, MM 2016. Proceedings of the 24th ACM International Conference on Multimedia, Amsterdam, The Netherlands, pp. 87–91 (2016). https://doi.org/10.1145/2964284.2967188
Li, M., Liu, J., Yang, W., Sun, X., Guo, Z.: Structure-revealing low-light image enhancement via robust Retinex model. IEEE Trans. Image Process. 27(6), 2828–2841 (2018)
Rao, Z., Xu, T., Luo, J., Guo, J., Shi, G., Wang, H.: Non-uniform illumination endoscopic imaging enhancement via anti-degraded model and \(L_1L_2\)-based variational retinex. EURASIP J. Wirel. Commun. Network. 2017(1), 1–11 (2017)
Xu, L., Yan, Q., Xia, Y., Jia, J.: Structure extraction from texture via relative total variation. ACM Trans. Graph. 31(6), 1–10 (2012)
Li, Y., Guo, F., Tan, R.T., Brown, M.S.: A contrast enhancement framework with JPEG artifacts suppression. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 174–188. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10605-2_12
Goldstein, T., Osher, S.: The split Bregman method for L1 regularized problems. SIAM J. Imaging Sci. 2(2), 323–343 (2009)
Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 391–407. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_25
Kodak Lossless True Color Image Suite. http://r0k.us/graphics/kodak/. Accessed 11 Aug 2019
Wang Z., Simoncelli, E.P., Bovik, A.C.: Multi-scale structural similarity for image quality assessment. In: Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers (2003). https://doi.org/10.1109/ACSSC.2003.1292216
Fu, X., Zeng, D., Huang, Y., Liao, Y., Ding, X., Paisley, J.: A fusion-based enhancing method for weakly illuminated images. Sig. Process. 129, 82–96 (2016)
Retinex Image Processing. https://dragon.larc.nasa.gov/retinex/pao/news/. Accessed 11 Aug 2019
Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012). A Publication of the IEEE Signal Processing Society
Mittal, A., Fellow, Soundararajan, R., Bovik, A.C.: Making a ‘completely blind’ image quality analyzer. IEEE Sig. Process. Lett. 20(3), 209–212 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Du, X., Xia, Y. (2020). Natural Images Enhancement Using Structure Extraction and Retinex. In: Blanc-Talon, J., Delmas, P., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2020. Lecture Notes in Computer Science(), vol 12002. Springer, Cham. https://doi.org/10.1007/978-3-030-40605-9_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-40605-9_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-40604-2
Online ISBN: 978-3-030-40605-9
eBook Packages: Computer ScienceComputer Science (R0)