Image Fusion Using Pca in Cs Domain

Abstract

Compressive sampling (CS), also called Compressed Sensing, has generated a tremendous amount of excitement in the image processing community. It provides an alternative to Shannon/Nyquist sampling when the signal under acquisition is known to be sparse or compressible. In this paper, we propose a new efficient image fusion method for compressed sensing imaging. In this method, we calculate the two-dimensional discrete cosine transform of multiple input images, these achieved measurements are multiplied with sampling filter, so compressed images are obtained. we take inverse discrete cosine transform of them. Finally, fused image achieves from these results by using PCA fusion method. This approach also is implemented for multi-focus and noisy images. Simulation results show that our method provides promising fusion performance in both visual comparison and comparison using objective measures. Moreover, because this method does not need to recovery process the computational time is decreased very much.