Improvement of Compressive Sampling Based Approaches in Images Fusion

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 thoughtpossible. In this paper, we propose an efficient image fusion method for compressed sensing imaging. First, we calculate thetwo-dimensional discrete cosine transform of multiple input images, these achieved measurements are multiplied with sampling filter, so compressed images are resulted. we takeinverse discrete cosine transform of these results and convert them to vectors. Now, fusion is performed on the waveletapproximation 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.