针对自然信号、图像中的丰富时序结构会影响基于多观测向量的压缩图像融合算法性能，基于块稀疏贝叶斯学习，构造了一种新的压缩图像融合算法.该算法采用概率性方法，利用正定矩阵模型化数据间的时序结构对图像中的时序结构进行建模，并将其统一在多观测向量模型中，进而通过贝叶斯规则和对超参量的估计，获取原始图像数据的最大后验估计.为验证该算法的有效性，对其进行了图像融合实验.仿真实验结果表明，与单观测向量模型下的压缩图像融合算法相比，所提出算法能有效降低所需的采样数量，且对多类图像都表现出更优的融合效果.

Natural signals and images usually have rich temporal structures, which greatly influence the performance of the compressive image fusion algorithms based multiple measurement vectors. In this paper, a new compressive image fusion algorithm was investigated based on block sparse Bayesian learning. The proposed algorithm used a probabilistic approach, and constructed the temporal structures of images via the positive definite matrices under the multiple measurement vectors model. Thus, the MAP estimate of original images were obtained according to the Bayes rule and the estimation of hyperparameters. To verify the applicability of the proposed method, numerical experiments of image fusion were performed. Numerical results indicate that the proposed method can obviously reduce the sampling number required, and provide better fusion performance for many kinds of images compared to algorithms based on single measurement vector model.