A new strategy for images fusion is developed on the basis of block compressed sensing (BCS) and multiwavelet transform (MWT). Since the BCS with structured random matrix requires small memory space and enables fast computation, firstly, the images with large amounts of data can be compressively sampled into block images for fusion. Secondly, taking full advantages of multiwavelet such as symmetry, orthogonality, short support, and a higher number of vanishing moments, the compressive sampling of block images can be better described by MWT transform. Then the compressive measurements are fused with a linear weighting strategy based on MWT decomposition. And finally, the fused compressive samplings are reconstructed by the smoothed projection Landweber algorithm, with consideration of blocking artifacts. Experiment result shows that the validity of proposed method. Simultaneously, field test indicates that the compressive fusion can give similar resolution with traditional MWT fusion.