Block-based Compressive Sensing Image Fusion Method Based on Particle Swarm Optimization Algorithm

摘要

This In order to solve the problem that the spatial matching is difficult and the spectral distortion is large in traditional pixel-level image fusion algorithm. In this paper, we proposed an block-based compressive sensing image fusion method based on particle swarm optimization algorithm. We get the compressive measurements of input images by block-based compressive sensing (BCS) and fused them with the rule of linear weighting, while the fusion coefficients ω(ω_1, ω_2, ω_3..., ω_n, n is the divided number of blocks of the image to be fused) of each block were selected by particle swarm optimization algorithm. In the iterative process, the image fusion coefficient ω_i is taken as particle, and the optimal value is obtained by combining the optimal objective function, taking the coefficient ω_i as the weight value. The algorithm ensures the optimal selection of fusion effect with a certain degree of self-adaptability. To evaluate the fused images, this paper uses five kinds of index parameters such as Entropy, Standard Deviation, Average Gradient, Degree of Distortion and Peak Signal-to-Noise Ratio. The experimental results show that the image fusion effect of the algorithm in this paper is better than that of traditional methods.