Improvement of compressive sampling based approaches in images fusion

Compressed Sensing (CS) is a new signal acquisition technique that allows sampling of sparse signals using significantly fewer measurements than previously thought possible. In this paper, we propose an efficient image fusion method for compressed sensing imaging. First, we calculate the two-dimensional discrete cosine transform of multiple input images, these achieved measurements are multiplied with sampling filter, so compressed images are resulted. we take inverse discrete cosine transform of these results and convert them to vectors. Now, fusion is performed on the wavelet approximation and detail coefficients of vectors separately. The fused vector receives from these fused coefficients. Finally, the fused vector arranges to the fused image. Simulation results show that our method provides promising fusion performance with a low computational complexity.