Image Fusion and Recognition based on Compressed Sensing Theory

As the compressed sensing theory can offer a better performance than Nyquist sampling theorem when dealing with large amounts of data, it becomes very popular for image fusion and target recognition in image processing. In this paper, a new image fusion algorithm based on compressed sensing was proposed. By discrete cosine transform, it fused images through weighted coefficient, recovered the fusion images by basic pursuit algorithm. Moreover, a recognition algorithm in compressed sensing was also studied, which obtained a sample matrix using preprocessing based on a wavelet transform, calculated the approximate coefficient by orthogonal matching pursuit, and made a classification using the with minimum distance formula. Finally, experiments were designed to demonstrate the effectiveness of the proposed algorithms.