Infrared Image Enhancement Based on Multi-Scale Cyclic Convolution and Multi-Clustering Space

LU Hao-xiang; LIU Zhen-bing; ZHANG Jing; WANG Zi-min

doi:10.12263/DZXB.20210177

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Infrared Image Enhancement Based on Multi-Scale Cyclic Convolution and Multi-Clustering Space

PAPERS | 更新时间：2025-12-08

- Infrared Image Enhancement Based on Multi-Scale Cyclic Convolution and Multi-Clustering Space
- ACTA ELECTRONICA SINICA Vol. 50, Issue 2, Pages: 415-425(2022)
- 作者机构：
  
  1.桂林电子科技大学计算机与信息安全学院，广西桂林 541004
  2.桂林电子科技大学商学院，广西桂林 541004
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20210177
  CLC： TP391
- Received：27 January 2021，
  
  Revised：2021-09-29，
  
  Published：25 February 2022
- 稿件说明：
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路皓翔,刘振丙,张静等.结合多尺度循环卷积和多聚类空间的红外图像增强[J].电子学报,2022,50(02):415-425.

LU Hao-xiang,LIU Zhen-bing,ZHANG Jing,et al.Infrared Image Enhancement Based on Multi-Scale Cyclic Convolution and Multi-Clustering Space[J].ACTA ELECTRONICA SINICA,2022,50(02):415-425.
路皓翔,刘振丙,张静等.结合多尺度循环卷积和多聚类空间的红外图像增强[J].电子学报,2022,50(02):415-425. DOI： 10.12263/DZXB.20210177.

LU Hao-xiang,LIU Zhen-bing,ZHANG Jing,et al.Infrared Image Enhancement Based on Multi-Scale Cyclic Convolution and Multi-Clustering Space[J].ACTA ELECTRONICA SINICA,2022,50(02):415-425. DOI： 10.12263/DZXB.20210177.

摘要

红外图像低对比度、低亮度及轮廓细节模糊等问题限制了红外成像技术的应用.为了提高红外图像的质量，本文提出了一种结合多尺度循环卷积和多聚类空间红外图像增强方法，该方法分为五个阶段：多尺度循环卷积、多聚类空间混合均衡化、多角度组合滤波器、线性融合和图像层次化处理.其中，通过多尺度循环卷积和多角度组合滤波器增强图像细节及轮廓信息；多聚类空间混合均衡化通过改进的K-means对图像灰度值进行聚类分析，依据图像特性选取不同的均衡化方式提升图像对比度及亮度；图像层次化处理用于提升图像清晰度.结果表明：与对比方法相比，该方法能够显著增强红外图像细节及轮廓，提升红外图像对比度和亮度.

Abstract

Infrared image has problems of low contrast

low brightness and blurring of contour details

limiting the application of the infrared imaging technology. To improve the quality of infrared image

an infrared image enhancement method based on multi-scale cyclic convolution and multi-clustering space is proposed. The method contains five stages: multi-scale cyclic convolution

hybrid equalization in the clustering space

multi-angle combination filter

linear fusion

and image hierarchical processing. The details and contour information of the image are enhanced by multi-scale cyclic convolution and multi-angle combination filter

and the hybrid equalization of the clustering space performs clustering analysis on the image gray value through the improved K-means. According to the image characteristics

different equalization methods are selected to improve the contrast and brightness of the image. The hierarchical processing of image is used to improve clarity of the image. Experimental results show that the proposed method not only can improve the details and contour of infrared image

but also can enhance the contrast and brightness of infrared image compared with the contrast method.

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references

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