基于压缩感知的红外与可见光图像融合算法

摘要

Compressive sensing is a novel signal sampling theory,according to Nyquist sampling theory,the sampling rate of the signal must be greater than twice of the maximum signal frequency.For a sparse representation signal,its sampling rate is far below the Nyquist sampling rate,and the signal can be obtained by reconstructed algorithm.A new fusion algorithm for infrared and visible image is proposed based on compressive sensing,source image is measured by random matrix,and measured value is fused by fusion algorithm.The experimental results show that compressive sens-ing theory can obtain fusion image effectively.%压缩感知是一种新的信号采样理论，突破了传统的Nyquist采样率须为信号最高频率的2倍以上的定理。对于稀疏信号，它能够以远低于Nyquist采样速率对信号进行采样，并通过重构算法恢复出原信号。提出了一种基于压缩感知的红外与可见光图像融合算法，对图像进行测量，并通过融合算法对测量值进行融合。仿真实验显示，压缩感知能较好地实现图像的融合。