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Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 8, Issue 1, Pages 159-180, DOI: https://doi.org/10.21307/ijssis-2017-753
License : (CC BY-NC-ND 4.0)
Received Date : 30-October-2014 / Accepted: 08-January-2015 / Published Online: 01-March-2015
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.
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