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工程科学与技术:2022,54(5):228-239
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基于梯度稀疏和多尺度变分约束的图像增强算法
(上海电力大学 自动化工程学院,上海 200090)
Image Enhancement Algorithm Based on Gradient Sparsity and Multi-scale Variational Constraint
(School of Automation Eng., Shanghai Univ. of Electric Power, Shanghai 200090, China)
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投稿时间:2021-06-20    修订日期:2022-04-05
中文摘要: 针对低照度条件下采集到的图像存在亮度偏低、细节模糊等问题,通过分析传统Retinex理论在增强图像过程中的缺陷,提出了一种基于梯度稀疏和多尺度变分约束的图像增强算法。该算法首先将输入图像由RGB空间转换到HSV空间,提取亮度分量,实现3个通道的解耦合。然后,根据零范数的梯度全局显著特性,定义了一个新的相对全变分正则项。接着,在HSV空间下惩罚亮度分量,构建一种具有梯度稀疏的变分模型对亮度通道进行约束,并通过将控制因子扩张为多个尺度,形成多尺度变分约束,提升照度估计的准确度,使之更加符合光照分布特性。根据Retinex理论进行映射,获取亮度通道对应的反射图像。进而利用亮度通道不同尺度下的约束所对应的不同照度结果,分别提取图像的粗略细节、中等细节和精细细节,通过多尺度细节加权,对反射图像进行细节增强。最后,对照度图像进行伽马校正,与经细节提升后的反射图像重组并进行颜色空间转换得到输出的增强图像。通过定量、定性实验对本文算法进行了验证,实验结果表明:本文算法的增强图像有着更高的色彩丰富度和更低的色差水平,能够保持图像的自然度,提升图像的视觉效果。在均值、平均梯度和信息熵的表现上,相比原图均有大幅度提升;与现有的先进算法相比,本文算法的平均定量指标在不同类型低照度图像的增强图像上均产生了较优的效果,且运算效率较高。
Abstract:Aiming at the problems of low brightness and blurred details in images captured under low light, an image enhancement algorithm based on gradient sparsity and multi-scale variational constraint was proposed by analyzing the defects of traditional Retinex theory. Firstly, the input image was transformed from RGB space to HSV space, and the luminance component was extracted to decouple three channels. Then, according to the gradient global significance of zero norm, a new relative total variation regular term was defined. After that, the luminance component was punished in HSV space, and a variational model with gradient sparsity was constructed to constrain the brightness channel. By expanding the control factors to multiple scales, a multi-scale variational constraint was formed, which improves the accuracy of illumination estimation and makes it more in line with the illumination distribution characteristics. According to the Retinex theory, areflection map corresponding to brightness channel was obtained. Then, the rough details, medium details and fine details of the image were extracted by using different illumination results corresponding to constraints in different scales of brightness channel, and the details of the reflection map was enhanced by multi-scale detail weighting. Finally, the illumination map after Gamma correction was recombined with the enhanced reflection ma, and the color space conversion was carried out to obtain the output enhanced image. Experimental comparisons show that the enhanced images of the proposed algorithm visually have richer colors richness and lower color difference level, and keep the naturalness well. Compared with the original images, the performance of mean value, average gradient and information entropy have been greatly improved. Compared with the existing advanced algorithms, the average quantitative index of the proposed algorithm achieves a better effect on the enhanced images of different types of low-light images with higher computational efficiency.
文章编号:202100576     中图分类号:TP391.41    文献标志码:
基金项目:上海市电站自动化技术重点实验室项目(13DZ2273800)
作者简介:第一作者:黄福珍(1976—),女,副教授,博士. 研究方向:计算机视觉;智能信息处理. E-mail:huangfzh@shiep.edu.cn;通信作者:王奎, E-mail:kwangsuep@163.com
引用文本:
黄福珍,王奎.基于梯度稀疏和多尺度变分约束的图像增强算法[J].工程科学与技术,2022,54(5):228-239.
HUANG Fuzhen,WANG Kui.Image Enhancement Algorithm Based on Gradient Sparsity and Multi-scale Variational Constraint[J].Advanced Engineering Sciences,2022,54(5):228-239.