基于子带分解的MSR雾天图像增强算法

doi:DOI:10.3969/j.issn.1007-2985.2015.01.010

journal6 ›› 2015, Vol. 36 ›› Issue (1): 40-45.DOI: DOI:10.3969/j.issn.1007-2985.2015.01.010

基于子带分解的MSR雾天图像增强算法

舒婷，刘耀峰，邓波，谭亚鹏，陈炳权

（吉首大学物理与机电工程学院，湖南吉首 416000）

出版日期:2015-01-25 发布日期:2015-01-29
通讯作者: 陈炳权（1972-），男，湖南桃源人，吉首大学物理与机电工程学院副教授，工学博士，硕导，主要从事信号处理与智能控制研究.
基金资助:
湖南省教育厅科学研究资助项目（14C0920）舒婷（1989—），女，湖南娄底人，吉首大学物理与机电工程学院无线电物理硕士研究生，主要从事信号处理与模式识别研究.

Multi-scale Retinex Algorithm for the Foggy Image Enhancement Based on Sub-band Decomposition

SHU Ting, LIU Yao-Feng, DENG Bo, TAN Ya-Peng, CHEN Bing-Quan

(College of Physics and Mechanical & Electrical Engineering,Jishou University,Jishou 416000,Hunan China)

Online:2015-01-25 Published:2015-01-29

摘要/Abstract

摘要：提出了一种基于子带分解的MSR的图像增强算法(Subband-Decomposed Multiscale Retinex,简称SDMSR).先利用改进后的MSR算法对雾天图像进行处理，之后由子带分解的方法对雾天图像的不同子带分解输出进行增益，最后采用图像融合技术得到清晰的增强图像.并借助均值、标准差、熵、均方误差(MSE)和峰值信噪比(PSNR)对文中算法的仿真结果进行了定量评价，仿真与评价结果表明，该算法能够提高雾天图像的对比度，保留了原图像中有用的信息，图像的失真程度小，图像的清晰度也得到了提高.

关键词： 子带分解, Retinex理论, SDMSR算法, 均值, 标准差, MSE, PSNR

Abstract: A kind of Multi-Scale Retinex (MSR) algorithm based on sub-band decomposition for image enhancement was proposed.First,the improved MSR algorithm was used to deal with the foggy image.Then,the gain of the different foggy image was obtained by the sub-band decomposition method.After that,the clear and enhanced image was obtained by the image fusion technology.At the same time,the simulation results of the algorithm was analyzed by using the mean value,standard deviation,entropy and mean square error (MSE) and peak signal-to-noise ratio (PSNR).The simulation and evaluation results show that the contrast of image is increased,the useful information of the original image is retained,the degree of distortion is small,and the clarity of the image can be improved.

Key words: sub-band decomposition, Retinex algorithm, sub-band decomposition multi-scale retinex (SDMSR), mean value, standard deviation, mean squared error(MSE), peak signal-to-noise ratio(PSNR)

舒婷, 刘耀峰, 邓波, 谭亚鹏, 陈炳权. 基于子带分解的MSR雾天图像增强算法[J]. journal6, 2015, 36(1): 40-45.

SHU Ting, LIU Yao-Feng, DENG Bo, TAN Ya-Peng, CHEN Bing-Quan. Multi-scale Retinex Algorithm for the Foggy Image Enhancement Based on Sub-band Decomposition[J]. journal6, 2015, 36(1): 40-45.

参考文献

[1] LAND E.The Retinex[J].Am Sci,1964,52(2):247-264.

[2] RAHMAN Z,JOBSON D J,WOODELL G A.Multi-Scale Retinex for Color Image Enhancement[C]//Image Processing,1996.Proceedings.,International Conference on.IEEE,1996,3:1 003-1 006.

[3] JOBSON D J,RAHMAN Z,WOODELL G A.Properties and Performance of a Center/surround Retinex[J].Image Processing,IEEE Transactions on,1997,6(3):451-462.

[4] MOORE A,ALLMAN J,GOODMAN R M.A Real-Time Neural System for Color Constancy[J].Neural Networks,IEEE Transactions on,1991,2(2):237-247.

[5] JOBSON D J,RAHMAN Z,WOODELL G A.A Multi-Scale Retinex for Bridging the Between Color Images and the Human Observation of Scenes[J].Ieee Transactions on Image Processing for Automatic Image Enhancement [J].Journal of Electronic Imaging,2004,13(1):100-110.

[6] RAHMAN Z.Investigating the Relationship Between Image Enhancement and Image Compression in the Context Of The Multi-Scale Retinex[J].Journal of Visual Communication and Image Representation,2011,22(3):237-250.

[7] 方志军，等.基于子带加权融合的多尺度Retinex图像增强算法[J].山东大学学报:理学版,2013(48).

[8] JANG J H,CHOI B,KIM S D,et al.Sub-Band Decomposed Multiscale Retinex with Space Varying Gain[C]//Image Processing,2008.ICIP 2008.15th IEEE International Conference on.IEEE,2008:3 168-3 171.

[9] JANG J H,KIM S D,RA J B.Enhancement of Optical Remote Sensing Images by Subband-Decomposed Multiscale Retinex with Hybrid Intensity Transfer Function[J].Geoscience and Remote Sensing Letters,IEEE,2011,8(5):983-987.

[10] JANG J H,BAE Y,RA J B.Contrast-Enhanced Fusion of Multisensory Images Using Subband-Decomposed Multiscale Retinex[J].Image Processing,IEEE Transactions on,2012,21(8):3479-3490.

[11] BJONTEGAARD G.document VCEG-M33:Calculation of Average PSNR Differences Between RD-Curves[C]//ITU-T VCEG Meeting,Austin,Texas,USA,Tech.Rep.2001.

[12] 赵小川.现代数字图像处理技术提高及应用案例的详解（MATLAB版）[M].北京：北京航空航天大学出版社，2011：112-132.

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基于子带分解的MSR雾天图像增强算法

Multi-scale Retinex Algorithm for the Foggy Image Enhancement Based on Sub-band Decomposition

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