Abstract: Based on Compressed Sensing（CS）, a new image fusion method is presented. The new method can take reduced samples, and has the advantages of simple structure and easy implementation. The method decomposes two or more original images using wavelet transform, and gets the sparse matrix by the wavelet coefficient sparse representation, and fuses the sparse matrices with the coefficients absolute value maximum scheme. Randomly observed, it can receive the compressed sample. At the fusing end, the fusion image is recovered from the reduced samples by solving the optimization. The proposed method can construct the fusion image with less measurements because the wavelet coefficients are sparse presentation. Simulation results show the proposed method exhibits its superiority over the traditional method of the maximum absolute values fusion with the same sampling rates, and under the lower sampling, it can also achieve better fusion performance.

Key words: compressed sensing, randomly observed, image fusion, absolute value maximum scheme

摘要： 基于压缩感知（Compressed Sensing，CS）理论，提出了一种采样点少且结构简单易实现的图像融合方法。对需要处理的两幅或多幅图像进行小波变换，分别对得到的小波系数进行稀疏处理得到稀疏矩阵，通过系数绝对值较大法进行融合，对融合后的系数矩阵通过随机观测获取压缩采样，而图像恢复则是对得到的压缩采样通过求解最优化的问题得到。由于对小波系数进行了稀疏处理，故该方法可以用少量的采样点来恢复图像。实验结果表明，在相同采样点下，该方法得到的图像质量（PSNR）明显优于传统的系数绝对值较大法融合；在少量采样点下，采用该方法也可以使融合的图像达到较好的效果。

关键词: 压缩感知, 随机观测, 图像融合, 绝对值较大法