基于广义线性系统和非锐化掩膜的图像增强算法

范日召; 何斌; 刘燕君; 李军凯; 陈洋君

doi:10.3788/YJYXS20163110.0973

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基于广义线性系统和非锐化掩膜的图像增强算法

显示与图像 | 更新时间：2020-07-07

- 基于广义线性系统和非锐化掩膜的图像增强算法
- Image enhancement algorithm based on generalized linear system and unsharp masking
- 液晶与显示 2016年31卷第10期页码：973-982
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所, 吉林长春 130033
  2.中国科学院大学, 北京 100049
  3.中国科学院深圳先进技术研究院, 广东深圳 518055
  4.东北大学, 辽宁沈阳 110819
- 作者简介：
  
  [ "范日召(1990-), 男, 河北邢台人, 硕士研究生, 研究方向为图像处理和光电成像技术。E-mail:fanrizhao@163.com" ]
  何斌(1961-), 男, 吉林长春人, 博士, 研究员, 主要研究方向卫星成像技术。E-mail:hbemail@163.com
- 基金信息：
  
  广东省引进创新科研团队项目(2011S013);Guangdong Province, Introduction of Innovation R & D Team(2011S013)
- DOI：10.3788/YJYXS20163110.0973
  中图分类号： TP911.73
- 收稿日期：2016-07-09，
  
  录用日期：2016-8-11，
  
  纸质出版日期：2016-10
- 稿件说明：
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范日召, 何斌, 刘燕君, 等. 基于广义线性系统和非锐化掩膜的图像增强算法[J]. 液晶与显示, 2016,31(10):973-982.

Ri-zhao FAN, Bin HE, Yan-jun LIU, et al. Image enhancement algorithm based on generalized linear system and unsharp masking[J]. Chinese journal of liquid crystals and displays, 2016, 31(10): 973-982.
范日召, 何斌, 刘燕君, 等. 基于广义线性系统和非锐化掩膜的图像增强算法[J]. 液晶与显示, 2016,31(10):973-982. DOI： 10.3788/YJYXS20163110.0973.

Ri-zhao FAN, Bin HE, Yan-jun LIU, et al. Image enhancement algorithm based on generalized linear system and unsharp masking[J]. Chinese journal of liquid crystals and displays, 2016, 31(10): 973-982. DOI： 10.3788/YJYXS20163110.0973.

摘要

为了解决非锐化掩膜图像增强方法的噪声增益、光晕现象和数据溢出的问题，提出一种基于广义线性系统和非锐化掩膜的图像增强算法。算法首先采用双边滤波器和广义线性减法对图像进行处理，获取图像细节层和基础层；然后对图像细节层和基础层分别进行自适应增益处理和对比度增强处理；最后采用广义线性加法将两者相加，实现图像的增强。并对3种不同类型的图像进行测试，结果表明：本文提出的算法很好地抑制了噪声干扰和光晕现象，增强了图像的对比度和细节，视觉效果良好，信息熵分别提高了10.9012%、16.0143%和9.1878%。表明了提出的基于广义线性系统和非锐化掩膜图像增强算法的合理有效性。

Abstract

In order to solve the problem that unsharp masking suffers from noise over-enhancement

halo effect and data overflow

an image enhancement algorithm based on generalized linear system and unsharp masking is proposed. Firstly

the algorithm adopts bilateral filter and generalized subtraction to decompose image and obtains the detail layer and the root layer. Then the detail layer and the root layer are dealt with adaptive gain control and contrast enhancement operation

respectively. Finally

the generalized addition is adopted to carry out superposition to realize image enhancement. And the test experiments for three different types of images are carried out. The outcome shows that the proposed algorithm can get good results in restraining noise and halo effect

enhancing contrast and detail of image and obtaining fine visual effect. The entropy of images is increased by 10.9012%

16.014 3% and 9.1878% respectively. It indicates that the image enhancement algorithm based on generalized linear system and unsharp masking is reasonable and effective.

关键词

Keywords

references

陈永亮.灰度图像的直方图均衡化处理研究[D].合肥:安徽大学, 2014. http://cdmd.cnki.com.cn/Article/CDMD-10357-1014231091.htm

CHEN Y L. Gray image histogram equalization processing research[D]. Hefei:Anhui University, 2014. (in Chinese) https://en.wikipedia.org/wiki/Histogram_equalization

贺梦颖.医学图像的增强技术研究与仿真[D].太原:中北大学, 2015. http://cdmd.cnki.com.cn/Article/CDMD-10110-1015585418.htm

HE M Y. Research and simulation on medical image enhancement algorithm[D]. Taiyuan:North University of China, 2015. (in Chinese)

RAMPONI G. A cubic unsharp masking technique for contrast enhancement[J]. Signal Processing, 1998, 67(2):211-222.

陈莹, 朱明.多子直方图均衡微光图像增强及FPGA实现[J].中国光学, 2014, 7(2):225-233.

CHEN Y, ZHU M.Multiple sub-histogram equalization low light level image enhancement and realization on FPGA[J]. Chinese Optics, 2014, 7(2):225-233. (in Chinese)

GONZALEZ R C, WOODS R E. Digital Image Processing[M]. 2nd ed. Upper Saddle River, NJ:Prentice Hall, 2009.

冯策, 戴树岭.一种改进的非锐化掩模深度图像增强算法[J].哈尔滨工业大学学报, 2014, 46(8):107-112.

FENG C, DAI S L. An improved unsharp masking method for depth map enhancement[J]. Journal of Harbin Institute of Technology, 2014, 46(8):107-112. (in Chinese)

黄梅, 吴志勇, 梁敏华, 等.暗背景下低灰度图像的实时增强[J].液晶与显示, 2011, 26(3):374-378.

HUANG M, WU Z Y, LIANG M H, et al. Real-time enhancement method of low gray image under dark background[J]. Chinese Journal of Liquid Crystals and Displays, 2011, 26(3):374-378. (in Chinese)

焦竹青, 徐保国.基于同态滤波的彩色图像光照补偿方法[J].光电子激光, 2010, 21(4):602-605.

JIAO Z Q, XU B G. Color image illumination compensation based on homomorphic filtering[J]. Journal of Optoelectronics·Laser, 2010, 21(4):602-605. (in Chinese)

YANG C C.A modification for the mask-filtering approach by superposing anisotropic derivatives in an image[J]. Optik-International Journal for Light and Electron Optics, 2011, 122(18):1684-1687.

ZHOU J J, ZHOU F G. Single image dehazing motivated by Retinex theory[C]//20132nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA). Toronto:IEEE, 2013:243-247.

DENG G. Parametric genearlized linear system based on the notion of the t-norm[J]. IEEE on Image processing, 2013, 22(7):2903-2909.

DENG G. A generalized unsharp masking algorithm[J]. IEEE transactions on Image Processing, 2011, 20(5):1249-1261.

刘艳莉, 桂志国, 张权, 等.基于局部梯度和复杂度的反锐化掩膜法[J].仪器仪表学报, 2014, 35(7):1539-1545.

LIU Y L, GUI Z G, ZHANG Q, et al. Unsharp masking algorithm based on local gradient and complexity[J]. Chinese Journal of Scientific Instrument, 2014, 35(7):1539-1545. (in Chinese)

王玉灵.基于双边滤波的图像处理算法研究[D].西安:西安电子科技大学, 2010. (in Chinese) http://cdmd.cnki.com.cn/Article/CDMD-10701-2010083760.htm

WANG Y L.Study of Algorithm in image processing based on the bilateral filter[D]. XIAN:Xidian University, 2010.

TOMASI C, MANDUCHI R. Bilateral filtering for gray and color images[C]//Sixth International Conference on Computer Vision, 1998. Bombay:IEEE, 1998:839-846.

RUSSO F.An image enhancement technique combining sharpening and noise reduction[J]. IEEE Transactions on Instrumentation and Measurement, 2002, 51(4):824-828.

ZUIDERVELD K. Contrast limited adaptive histogram equalization[M]//HECKBERT P S. Graphics gems IV. San Diego:Academic Press Professional, Inc., 1994:474-485.

黄君.图像质量评价的研究[D].杭州:浙江理工大学, 2012. http://cdmd.cnki.com.cn/Article/CDMD-10338-1012372167.htm

HUANG J.The research on image quality assessment[D]. Hangzhou:Zhejiang Sci-Tech University, 2012. (in Chinese)

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