Images Fusion based on Block Compressed Sensing and Multiwavelet Transform

A novel strategy for images fusion is presented based on the block compressed sensing (BCS) and multiwavelet transform (MWT). Since the BCS requires small memory requirement and enables fast computation, the images with large amounts of data can be compressively sampled by the BCS. Secondly, taking full advantages of multiwavelet such as symmetry, orthogonality, short support, and a higher number of vanishing moments, the compressive measurements of images can be better represented by the MWT. Moreover, the compressive measurements are fused based on the coherence of MWT decomposition coefficients. And finally, the fused image is reconstructed by the minimization of total variance method, and an overlapped blocking technique is proposed to eliminate the block effects. Experiments result shows the validity of the proposed method. Simultaneously, results also indicate that the compressive fusion can produce better results than conventional fusion techniques such as the principle component analysis method, Laplacian pyramid-based method, and wavelet transform method.