Abstract:A new fusion method based on Compressed Sensing(CS) is proposed to solve storage and computation cost problems in traditional image fusion algorithms. Sparse representation coefficients of source images are obtained on the basis of overcomplete two-dimensional Discrete Cosine Transform(DCT) dictionary. Then the observed values which will be fused are got by applying random projection on the coefficients. The weights of each image block are calculated adaptively based on standard deviation method. Thus input image measurements are fused into composite measurements via weighted averaging. The fused image is reconstructed through improved gradient pursuit with modified stepsize. The simulation results show that, comparing with other fusion algorithms, the proposed method can achieve better performance on fusion results with less sampling numbers and low computational complexity.