火力与指挥控制2024,Vol.49Issue(11) :184-192.DOI:10.3969/j.issn.1002-0640.2024.11.025

基于主成分分析的自适应低光照图像增强算法

Adaptive Low-light Image Enhancement Algorithm Based on Principal Component Analysis

郭苗苗 胡红萍 白艳萍 宋娜
火力与指挥控制2024,Vol.49Issue(11) :184-192.DOI:10.3969/j.issn.1002-0640.2024.11.025

基于主成分分析的自适应低光照图像增强算法

Adaptive Low-light Image Enhancement Algorithm Based on Principal Component Analysis

郭苗苗 1胡红萍 1白艳萍 1宋娜1
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作者信息

  • 1. 中北大学数学学院,太原 030051
  • 折叠

摘要

针对低光照图像能见度低、颜色退化、噪声大等问题,提出了一种基于主成分分析的自适应低光照图像增强算法.将原始RGB图像转换为HSV色彩空间,提取V分量;再根据估计的光照分量调整自适应亮度增强函数的参数,并利用图像融合增强图像的V分量;将图像从HSV空间转换回RGB空间.实验结果表明,提出的算法能够保留低光照图像的细节,且能很好地平衡图像颜色.

Abstract

Aiming at the problems of low visibility,color degradation and noise of low-light images,etc.an adaptive low-light image enhancement algorithm based on principal component analysis is pro-posed.Firstly,the original RGB image is converted to HSV color space and the V component is extracted.Then the parameters of the adaptive brightness enhancement function is adjusted according to the esti-mated illumination component,and the V component of the image is enhanced by image fusion.Finally,the image is converted from HSV space back to RGB space.Experimental results show that the proposed algorithm retains the details of low-light images and balances image colors well.

关键词

Retinex理论/形态学梯度/主成分分析/图像融合

Key words

Retinex theory/morphological gradients/principal component analysis/image fusion

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出版年

2024
火力与指挥控制
火力与指挥控制研究会,火力与指挥控制专业情报网

火力与指挥控制

CSTPCDCSCD北大核心
影响因子:0.312
ISSN:1002-0640
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