General Framework of Compressive Sampling and its Applications for Signal and Image Compression A Random Approach
DOI:
https://doi.org/10.15415/jtmge.2015.61001Keywords:
Basis Function, Compressive Sampling, Incoherent Signal, l1-norm, Sparse SignalAbstract
Compressive sampling emerged as a very useful random protocol and has become an active research area for almost a decade. Compressive sampling allows us to sample a signal below Shannon Nyquist rate and assures its successful reconstruction if the signal is sparse. In this paper we used compressive sampling for arbitrary signal and image compression and successfully reconstructed them by solving l1 norm optimization problem. We also showed that compressive sampling can be implemented if signal is sparse and incoherent through simulations.
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Articles in Journal of Technology Mangement for Growing Economies by Chitkara University Publications are Open Access articles that are published with licensed under a Creative Commons Attribution- CC-BY 4.0 International License. Based on a work at https://tmg.chitkara.edu.in. This license permits one to use, remix, tweak and reproduction in any medium, even commercially provided one give credit for the original creation.
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Journal of Technology Mangement for Growing Economies by Chitkara University Publications is licensed under a Creative Commons Attribution 4.0 International License. Based on a work at https://tmg.chitkara.edu.in/ |
