Randomness, Coherence and Noise Robustness in Compressive Sensing
Publish place: Signal Processing and Renewable Energy، Vol: 4، Issue: 1
Publish Year: 1399
نوع سند: مقاله ژورنالی
زبان: English
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JR_SPRE-4-1_005
تاریخ نمایه سازی: 20 فروردین 1399
Abstract:
The theory of compressive sensing (CS) in contrast with well-known Nyquist sampling theorem was proposed. Sensing matrix and sparse matrix have key roles in perfect signal reconstruction by using either greedy algorithms like orthogonal matching pursuit (OMP) or -norm based methods. In this paper, different pairs as sensing and sparse matrices are evaluated in terms of randomness and coherence. Noiselet as a complex measurement matrix has low coherence with Haar wavelet, and so the recovered images by OMP in comparison with other measurement-sparse matrices are appropriate. But, because of complexity, it cannot be used for big size images. However, the pair structured random sensing matrix with values 0, 1 and Fourier sparse matrix which got the second rank in terms of coherence, approved to be a noise robust pair and showed a great potential to be used in CS.
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Authors
Haybert Markarian
Department of Electrical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.
Sedigheh Ghofrani
Department of Electrical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.