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基于主成分分析的散斑设计方法

Speckle Design Method Based on Principal Component Analysis

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摘要

基于压缩感知的计算关联成像中,散斑设计是高质量图像重构的关键。针对传统散斑生成方法存在冗余高、关联成像质量低的问题,提出了一种基于主成分分析的散斑设计方法。该方法通过线性映射将高维空间中的数据投影到低维空间中,使低维空间上的投影方差最大化。结合图像先验知识,通过样本训练方法得到一组测量矩阵,在低采样率下可提高成像质量。实验结果表明,与传统方法相比,在采样率相同且低于0.5时,本方法可将图像的峰值信噪比提升5 dB,结构相似度提升0.2,为低采样率下获取高质量图像的同类场景提供了新思路。

Abstract

Speckle design is the key to high quality image reconstruction in compressive sensing based computational correlation imaging. Aiming at the problems of high redundancy and low quality of correlation imaging in traditional speckle pattern generation methods, we propose a speckle design method based on principal component analysis (PCA). In this method, the data in the high-dimensional space are projected into the low-dimensional space. Combined with image prior knowledge, a set of measurement matrixes are obtained by sample training method, which can improve the image quality at low sampling rate. The experimental results show that, compared with traditional methods, when the sampling rate is the same and lower than 0.5, this method can increase the peak signal-to-noise ratio of the image by 5 dB, and the structural similarity can be increased by 0.2. It provides a new idea for similar scenes that obtain high-quality images at low sampling ratio.

广告组1 - 空间光调制器+DMD
补充资料

中图分类号:O436

DOI:10.3788/LOP57.201104

所属栏目:成像系统

基金项目:国家自然科学基金;

收稿日期:2020-01-17

修改稿日期:2020-03-06

网络出版日期:2020-10-01

作者单位    点击查看

周栋:北京理工大学光电学院机器人与系统教育部重点实验室, 北京 100081
曹杰:北京理工大学光电学院机器人与系统教育部重点实验室, 北京 100081
姜雅慧:北京理工大学光电学院机器人与系统教育部重点实验室, 北京 100081
冯永超:中电科仪器仪表有限公司, 山东 青岛 266555
郝群:北京理工大学光电学院机器人与系统教育部重点实验室, 北京 100081

联系人作者:曹杰(ajieanyyn@163.com)

备注:国家自然科学基金;

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引用该论文

Zhou Dong,Cao Jie,Jiang Yahui,Feng Yongchao,Hao Qun. Speckle Design Method Based on Principal Component Analysis[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201104

周栋,曹杰,姜雅慧,冯永超,郝群. 基于主成分分析的散斑设计方法[J]. 激光与光电子学进展, 2020, 57(20): 201104

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