Infrared image enhancement based on variational partial differential equations

ZHAO Wen-da; ZHAO Jian; HAN Xi-zhen; XU Zhi-jun

doi:10.3788/YJYXS20142902.0281

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Infrared image enhancement based on variational partial differential equations

Table of contents | 更新时间：2020-08-12

- Infrared image enhancement based on variational partial differential equations
- Chinese Journal of Liquid Crystals and Displays Vol. 29, Issue 2, Pages: 281-285(2014)
- 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,中国,130033
  2. 中国科学院大学北京,中国,100049
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20142902.0281
  CLC： TP391
- Received：01 March 2013，
  
  Revised：13 April 2013，
  
  Published：05 March 2014
- 稿件说明：
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赵文达, 赵建, 韩希珍, 续志军. 基于变分偏微分方程的红外图像增强算法研究[J]. 液晶与显示, 2014,29(2): 281-285

ZHAO Wen-da, ZHAO Jian, HAN Xi-zhen, XU Zhi-jun. Infrared image enhancement based on variational partial differential equations[J]. , 2014,29(2): 281-285
赵文达, 赵建, 韩希珍, 续志军. 基于变分偏微分方程的红外图像增强算法研究[J]. 液晶与显示, 2014,29(2): 281-285 DOI： 10.3788/YJYXS20142902.0281.

ZHAO Wen-da, ZHAO Jian, HAN Xi-zhen, XU Zhi-jun. Infrared image enhancement based on variational partial differential equations[J]. , 2014,29(2): 281-285 DOI： 10.3788/YJYXS20142902.0281.

摘要

针对红外图像具有对比度低、边缘模糊的缺点，提出一种基于变分偏微分方程理论的红外图像增强方法。通过重建图像梯度场的分布，以此增强图像中纹理细节不明显、对比度小的微弱信息。在梯度场的变换中对噪声的幅值切割，完成了对噪声处理。结果表明本算法使得原图像的灰度平均梯度值提高2~3倍，对增强红外图像的边缘和微弱有用信息效果明显，避免了直方图均衡类算法丢失一些灰度相近且分布较少的微弱细节信息的缺点，为图像识别和跟踪应用提供了高质量的图像信息。

Abstract

Infrared images typically have the problems of low-contrast and blur edges. This article introduces the variation theory of partial differential equations to enhance the infrared images. By reconstructing image gradient field distribution

detailed texture and low-contrast can be improved. The processing of noise consists the cut of amplitude in the transform of gradient field. Based on experiment results

the original image gray gradient can be increased by 2~3 times and therefore significantly enhances the faint information and alleviates the edge effects. This approach can effectively overcome the problem of losing similar gray scale distribution caused by the histogram class algorithms. Therefore

it gives qualified image information for image recognition and tracking application.

关键词

Keywords

references

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