Haybert Markarian - Electrical Engineering Department South Tehran Branch, Islamic Azad University Tehran, Iran
Alireza Mohammad Zaki - Electrical Engineering Department South Tehran Branch, Islamic Azad University Tehran, Iran
Sedigheh Ghofrani - Electrical Engineering Department South Tehran Branch, Islamic Azad University Tehran, Iran

The emerging theory of compressive sensing (CS)is an alternative to Shannon/Nyquist sampling theorem speciallyin case of big data size applications. Perfect reconstruction ofundersampled data in CS framework is highly dependent toincoherence of measurement and sparsifying basis matriceswhich is usually fulfilled by selecting a random measurementmatrix. Noiselets as a measurement matrix have a very lowcoherence with wavelets which is the interest of CS, but up tonow they have not been compared with other well knownGaussian and Bernoulli measurement matrices from randomnessview point. So the main purpose of this paper is to introduce theNoiselets and compare them with other measurement matrices intwo point of view; randomness and recovered images.

Compressive sensing (CS), Noiselets, Gaussianmeasurement, Bernoulli measurement, randomness