计算机科学 ›› 2019, Vol. 46 ›› Issue (8): 327-331.doi: 10.11896/j.issn.1002-137X.2019.08.054
潘卫琼, 涂娟娟, 干宗良, 刘峰
PAN Wei-qiong, TU Juan-juan, GAN Zong-liang, LIU Feng
摘要: 在夜间采集到的图像由于受强灯光的影响,对比度较大,白天采集到的背光图像也是如此。对比度增强算法是常用的获得良好对比度图像的方法,但是这往往会造成亮区域过度增强的现象。为了解决对比度较大的这部分图像过度增强的问题,提出了一种基于Retinex自适应反射分量估计和对数图像处理减法后处理的低照度图像增强算法,该算法分为两部分:反射分量估计,基于对数图像处理减法(LIPS)模型的对比度增强。首先,用自适应双边滤波器代替传统的高斯滤波器来获得更精准的照明层。然后,根据最小可觉差(JND)阈值得到一个自适应因子来为对数域的照明分量加权,从而估计出图像的反射分量。这种方法可以有效防止高亮度区域的过度增强。最后,将基于标准偏差最大化的LIPS方法作用在反射层以增强图像的对比度,其中LIPS的参数范围由反射图像的累积分布函数(CDF)来确定。实验结果表明,文中所提算法在主观评价以及客观评价方面都优于其他对比算法。
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[1]KAUR M,VERMA K.A Novel Hybrid Technique for Low Exposure Image Enhancement using Sub-image Histogram Equalization and Artificial Neural Network[C]∥International Conference on Inventive Computation Technologies.IEEE,2017:1-5. [2]WANG S,ZHENG J,HU H,et al.Naturalness Preserved Enhancement Algorithm for Non-uniform Illumination Images [J].IEEE Transactions on Image Processing,2013,22(9):3538-3548. [3]LI L,WANG R,WANG W,et al.A Low-light Image Enhancement Method for Both Denoising and Contrast Enlarging [C]∥2015 IEEE International Conference on Image Processing.Quebec City,Canada,2015:3730-3734. [4]PANETTA K A,WHARTON E J,AGAIAI S S,et al.Human visual system-based image enhancement and logarithmic contrast measure [J].IEEE Transactions on Systems,Man,and Cybernetics,Part B (Cybernetics),2008,38(1):174-188. [5]LAND EH,The Retinex [J].American Scientist,1964,52(2):247-264. [6]JOBSON D J,RAHMAN Z,WOODELL G A,et al.Properties and Performance of a Center/surround Retinex [J].IEEE Transactions on Image Processing,1997,6(3):451-462. [7]RAHMAN Z,JOBSON D J,WOODELL G A,et al.Multi-scale Retinex for Color Image Enhancement [C]∥Proceedings of 3rd International Conference on Image Processing.Lausanne,Swit-zerland,1996:1003-1006. [8]JOBSON D J,RAHMAN Z,WOODELL G A,et al.A Multiscale Retinex for Bridging the Gap between Color Images and the Human Observation of Scenes [J].IEEE Transactions on Image Processing,1997,6(7):965-976. [9]KIMMEL R,ELAD M,SHAKED D,et al.A Variational Framework for Retinex [J].International Journal of Computer Vision,2003,52(1):7-23. [10]NG MK,WANG W.A Total Variation Model for Retinex [J].SIAM Journal on Imaging Sciences,2011,4(1):345-365. [11]FU X,ZENG D.A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation [C]∥Proceedings of the IEEE conference on Computer Vision and Pattern Recognition.Las Vegas,NV,USA,2016:2782-2790. [12]JOURLIN M,PINOLI J C.A Model for Logarithmic Image Processing [J].Journal of Microscopy,1988,149:21-35. [13]JOURLIN M,PINOLI J C,ZEBOUD R,et al.Contrast Definition and Contour Detection for Logarithmic Images [J].Journal of Microscopy,1989,156(1):33-40. [14]ZHAO Z,ZHOU Y.Comparative Study of Logarithmic Image Processing Models for Medical Image Enhancement [C]∥Proceedings of the IEEE International Conference on Systems,Man and Cybernetics.Budapest,Hungary,2016:001046-001050. [15]HAWKES PW.Logarithmic Image Processing:Theory and Applications[M].Academic Press,2016:1-259. [16]MEYLAN L,SUSSTRUN S.High Dynamic Range Image Rendering with A Retinex-based Adaptive Filter [J].IEEE Tran-sactions on Image Processing,2006,15(9):2820-2830 [17]XU K,JUNG C.Retinex-based Perceptual Contrast Enhance- ment in Images using Luminance Adaptation [C]∥Proceedings of the IEEE International Conference on Acoustics,Speech and Signal Processing.New Orleans,LA,USA,2017:1363-1367. [18]TOMASI C,MANDUCHI R.Bilateral Filtering for Gray and Color Images [C]∥Proceedings of Sixth International Confe-rence on Computer Vision.Bombay,India,1998:839-846. [19]BARTEN P G J.Contrast Sensitivity of the Human Eye and Its Effects on Image Quality [M].WA:SPIE Press,1999. [20]JAYANT N.Signal Compression:Technology Targets and Research Directions [J].IEEE Journal on Selected Areas in Communications,1992,10(5):796-818. [21]MITTAL A.SOUNDARARAJAN R,BOVIK AC,et al.Making a “Completely Blind” Image Quality Analyzer[J].IEEE Signal Processing Letters,2013,20(3):209-212. |
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