Local Fractional Differential Algorithm for Image Enhancement

Chen Qing-Li; HUANG Guo; MENG Tao

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Local Fractional Differential Algorithm for Image Enhancement

更新时间：2023-11-14

- Local Fractional Differential Algorithm for Image Enhancement
- Advanced Engineering Sciences Vol. 48, Issue 4, Pages: 115-122(2016)
- 作者机构：
  
  乐山师范学院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2016，
  
  Published Online：22 May 2016，
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陈庆利,黄果,门涛.数字图像的局部分数阶微分增强[J].工程科学与技术,2016,48(4):115-122.

Chen Qing-Li, HUANG Guo, MENG Tao. Local Fractional Differential Algorithm for Image Enhancement. [J]. Advanced Engineering Sciences 48(4):115-122(2016)
陈庆利,黄果,门涛.数字图像的局部分数阶微分增强[J].工程科学与技术,2016,48(4):115-122. DOI：

Chen Qing-Li, HUANG Guo, MENG Tao. Local Fractional Differential Algorithm for Image Enhancement. [J]. Advanced Engineering Sciences 48(4):115-122(2016) DOI：

摘要

中文摘要: 为了扩展传统分数阶微分在图像增强处理中微分阶次的范围，改善传统分数阶微分对图像亮度增强不甚理想的问题，提出了一种局部分数阶微分增强图像算法。首先根据局部分数阶微分理论，建立了数字图像的分数阶微分增强分算法。然后构造了新的数字图像分数阶微分增强模板，在该模板中增加亮度控制函数得到边缘、纹理和对比度同时增强的分数阶微分增强算法。实验表明，该方法能扩大分数阶微分在图像增强处理中阶次的范围；不但能很好地增强图像的边缘、纹理和轮廓等信息，又能明显改善图像的对比度和亮度，增强图像的视觉效果优于传统的分数阶微分增强方法的视觉效果。

Abstract

Abstract:In order to extend range of fractional differential order and to improve brightness when fractional differential methods are applied to image enhancement

a local fractional differential algorithm for image enhancement is proposed. First

according to theory of local fractional differential

a local fractional differential algorithm for image enhancement is deduced. Then

on basis of a new constructed fractional differential template for image enhancement

a new template joined a set of brightness control functions for texture

edge and brightness enhancement is gotten. Experiments and results show that the proposed method can extend the range of fractional differential order

and it not only can enhance edges

textures and other details of the image

but also can significantly improve the contrast and lightness of the image; experiments also show that the visual effect of enhanced images is better than that of traditional fractional differential-based methods.

关键词

图像增强分数阶微分局部分数阶微分分数阶微分模板对比度增强

Keywords

image enhancementfractional differentiallocal fractional derivativefractional differential maskcontrast enhancement

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