Markarian, H., Mohammad Zaki, A., Ghofrani, S. (2017). Noiselet Measurement Matrix Usage in CS Framework. Signal Processing and Renewable Energy, 1(1), 1-9.
Haybert Markarian; Alireza Mohammad Zaki; Sedigheh Ghofrani. "Noiselet Measurement Matrix Usage in CS Framework". Signal Processing and Renewable Energy, 1, 1, 2017, 1-9.
Markarian, H., Mohammad Zaki, A., Ghofrani, S. (2017). 'Noiselet Measurement Matrix Usage in CS Framework', Signal Processing and Renewable Energy, 1(1), pp. 1-9.
Markarian, H., Mohammad Zaki, A., Ghofrani, S. Noiselet Measurement Matrix Usage in CS Framework. Signal Processing and Renewable Energy, 2017; 1(1): 1-9.
1Electrical Engineering Department
South Tehran Branch, Islamic Azad University
Tehran, Iran
2Electrical Engineering Department South Tehran Branch, Islamic Azad University Tehran, Iran
Receive Date: 27 October 2017,
Accept Date: 12 November 2017
Abstract
Theory of compressive sensing (CS) is an alternative to Shannon/Nyquist sampling theorem which explained the number of samples requirement in order to have the perfect reconstruction. Perfect reconstruction of undersampled data in CS framework is highly dependent to incoherence of measurement and sparsifying basis matrices which the posterior is usually fulfilled by selecting a random matrix. While Noiselets, as a measurement matrix, have very low coherence with wavelets which are the interest of CS, they have never been studied well and compared with other well known Gaussian and Bernoulli measurement matrices, which have been widely used in CS framework, from randomness view point. Therefore, the main contribution of this paper is introducing Noiselets and comparing them with other measurement matrices in two point of view; randomness and quality of recovered images. In case of randomness, the entropy is used as a criterion for computing the randomness. In case of recovered images, the OMP and PDIP algorithms are applied under sampling rates 30, 40, 60%.
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