基于曲波变换的弱光照包装图像对比度设计增强方法
Contrast design and enhancement method of packaging image in weak light based on curvelet transform
丁超群1
作者信息
摘要
弱光照包装图像分辨率低的情况下,包装识别和检测的辨识度不高,需要进行对比度增强处理,提出基于曲波变换的弱光照包装图像对比度设计增强方法.构建图像空间特征信息检测模型,通过全色锐化方法多尺度分解图像,采用曲波变换方法重组全色锐化特征,结合字典集稀疏表示方法实现低维子空间表示,根据子空间细节表达结构构建弱光照包装图像对比度增强模型.结果表明,采用该方法进行弱光照包装图像对比度增强处理,提高了图像的辨识度水平和细节特征表达能力,辨识误差较低,平均为0.045,峰值信噪比较高,平均为42.134 dB,图像增强耗时平均为239.75 ms.
Abstract
In the case of low resolution packaging images in weak light,the identification degree of packaging rec-ognition and detection is not high,and contrast enhancement processing is required.A method for enhancing the con-trast design of packaging images in weak light based on curved wave transformation is proposed.Construct an image spatial feature information detection model,decompose the image using a panchromatic sharpening method at multiple scales,reorganize the panchromatic sharpening features using a curved wave transformation method,and combine the dictionary set sparse representation method to achieve low dimensional subspace representation.Construct a contrast enhancement model for weakly illuminated packaging images based on the detailed representation structure of the sub-space.The simulation results show that using this method to enhance the contrast of weak light packaging images im-proves the level of image recognition and the ability to express detailed features.The recognition error is low,with an average of 0.045,and the peak signal to noise ratio is high,with an average of 42.134 dB,the average image en-hancement time is 239.75 ms.
关键词
曲波变换/弱光照包装图像/对比度增强/多尺度分解/锐化重组Key words
curvelet transform/low light packaging image/contrast enhancement/multi-scale decomposition/sharpening recombination引用本文复制引用
基金项目
福建省中青年教师教育科研项目(JAS20473)
出版年
2024
NETL
NSTL
万方数据