计算机工程

• 图形图像处理 • 上一篇    下一篇

基于1 ~ 2 阶分数阶微分的图像增强算法

李军成   

  1. (湖南人文科技学院数学系,湖南娄底417000)
  • 收稿日期:2014-03-14 出版日期:2015-02-15 发布日期:2015-02-13
  • 作者简介:李军成(1982 - ),男,讲师、博士研究生、CCF 会员,主研方向:数字图像处理,计算机辅助几何设计。
  • 基金项目:
    湖南省自然科学基金资助项目(13JJ6081);湖南人文科技学院省级重点建设学科基金资助项目。

Image Enhancement Algorithm Based on 1 ~ 2 order Fractional Differential

LI Juncheng   

  1. (Department of Mathematics,Hunan University of Humanities,Science and Technology,Loudi 417000,China)
  • Received:2014-03-14 Online:2015-02-15 Published:2015-02-13

摘要: 在利用分数阶微分进行图像增强时,现有方法大多是基于0 ~1 阶分数阶微分,而基于1 ~2 阶分数阶微分 的方法较少。为此,分析1 ~2 阶分数阶微分对图像增强的作用,基于1 ~2 阶分数阶微分构造一种用于图像增强的 掩模算子。实验结果表明,该算子优于常用的频域法和空域法,比现有的一些0 ~1 阶分数阶微分算子具有更好的图像增强效果。

关键词: 图像处理, 图像增强, 分数阶微分, 掩模算子, 1 ~2 阶微分, 0 ~1 阶微分

Abstract: The present fractional differential methods for image enhancement are mostly constructed based on 0 ~ 1 order fractional differential. There are rare papers discussing the image enhancement based on 1 ~ 2 order fractional differential. This paper analyses the effect of 1 ~ 2 order fractional differential to image enhancement,and constructs a mask operator for image enhancement based on 1 ~ 2 order fractional differential. Experimental results demonstrate that the presented operator not only has better image enhancement results than the commonly used frequency domain methods and spatial domain methods,but also has better image enhancement results than some present 0 ~ 1 order fractional differential operators.

Key words: image processing, image enhancement, fractional differentiation, mask operator, 1 ~2 order differential, 0 ~ 1 order differential

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