期刊文献+

基于视觉特性的Riemann-Liouville分数阶图像增强 被引量:3

A Riemann-Liouville Fractional Differential Image Enhancement AlgorithmBased on Human Visual Characteristics
在线阅读 下载PDF
导出
摘要 为了解决Grünwald-Letnikov分数阶微分算法对彩色图像增强容易产生色彩失真以及传统方法增强效果不明显的问题,在分数阶微分视觉模型的基础上提出了一种基于Riemann-Liouville(R-L)分数阶微分的数字图像增强算法。首先讨论了数字图像的分数阶微分视觉模型;接着在R-L分数阶微分方程的基础上构造了8个方向上的分数阶微分增强模板;并讨论了这些微分增强模板的数值运算规则;并在HSI色彩空间对I分量进行分数阶微分实现彩色图像的增强处理。实验表明本文方法具有非线性特性,对图像增强效果明显,且增强后的彩色图像无色彩失真现象。 In order to overcome the defects that enhancement effects of classical approaches are not obvious and the Grünwald-Letnikov(G-L) fractional differential operator could make color image distortion,a fractional differential image enhancement algorithm based on fractional-order receptive field model for gray and color image enhancment was presented.First,fractional-order receptive field of digital image was discussed.Next,a fractional-order differential convolution mask for image enhancement was constructed according to R-L fractional partial differential equation and its numerical implementation on eight directions was discussed in detail.This algorithm was applied in HSI color space and only the Intensity(I) component was processed for color image enhancement.The experiments showed that the proposed algorithm is non-linear and it can obviously enhance gray and color images.The enhanced images have no color distortion.
出处 《四川大学学报(工程科学版)》 EI CAS CSCD 北大核心 2012年第1期99-105,共7页 Journal of Sichuan University (Engineering Science Edition)
基金 国家自然科学基金资助项目(60972131) 四川省教育厅科研基金资助项目(11ZB132)
关键词 图像增强 分数阶微分 视觉特性 感受野 分数阶微分模板 image enhancement fractional difference visual characteristic receptive field fractional differential mask
  • 相关文献

参考文献18

  • 1Stark J A. Adaptive image contrast enhancement using generalizations of histogram equalization [ J ]. IEEE Transaction on Image Processing,2000,9(5):889 -896.
  • 2Ariel T, Dikbas S, Altunbasak Y. A histogram modification framework and its application for image contrast enhancement[ J ]. IEEE Transactions on Image Processing, 2009, 18 (9) :1921-1935.
  • 3Starck J L, Murtagh F, Cands E J, et al. Gray and color image contrast enhancement by the curvelet transform [ J ]. IEEE Transaction on Image Processing,2003,12 (6) :706-717.
  • 4Tang J, Peli E, Acton S. Image enhancement using a contrast measure in the compressed domain [ J ]. IEEE Signal Processing Letters,2003,10(10) :289-292.
  • 5Mallat S, Zhong S. Characterization of signal from multiscale Edges [ J]. IEEE Trans Pattern Analysis and Machine Intelligence, 1992,14 (7) :710-732.
  • 6Ji T L, Sundareshan M, Roehrig K H. Adaptive image contrast enhancement based on human visual properties [ J ]. IEEE Trans Med Image, 1994,13 (4) : 573-586.
  • 7Rodieck R W. Quantitative analysis of cat retinal ganglion cell response to visual stimuli[ J]. Vision Research, 1965,5 (11) :583 -601.
  • 8Land E H, McCann J J. Lightness and Retinex Theory [ J ]. Journal of the Optical Society of America, 1971,61 ( 1 ) : 1-11.
  • 9Land E H. Recent advances in retinex theory and some implication for cortical computations:color vision and the natural image[ C]//Proc NatL Aead Sci USA. 1983,80:5163-5169.
  • 10Jobson D J, Rahman Z, Woodell G A. Properties and performance of a center/surround Retinex [ J ]. IEEE Transac tions on Image Processing, 1997,6(3 ) :451-462.

二级参考文献10

共引文献99

同被引文献16

引证文献3

二级引证文献14

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部