<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p></abstract>

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.

Image Fusion and Recognition based on Compressed Sensing Theory

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"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...

Image Fusion and Recognition based on Compressed Sensing Theory

Data publikacji: 01 mar 2015

Zakres stron: 159 - 180

Otrzymano: 30 paź 2014

Przyjęty: 08 sty 2015

DOI: https://doi.org/10.21307/ijssis-2017-753

© 2015 Qiuchan Bai et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.