基于主成分分析的散斑设计方法

Speckle Design Method Based on Principal Component Analysis

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。

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.