Compare Performance of Recovery Algorithms MP, OMP, L1-Norm in Compressive Sensing for Different Measurement and Sparse Spaces

الموضوعات :

Bahareh Davoodi ¹ , Sedigheh Ghofrani ²

1 - Electrical Engineering Department South Tehran Branch, Islamic Azad University Tehran, Iran
2 - Electrical Engineering Department South Tehran Branch, Islamic Azad University Tehran, Iran

تاريخ الإرسال : 08 الأربعاء , ذو الحجة, 1438 تاريخ التأكيد : 08 الأربعاء , ذو الحجة, 1438 تاريخ الإصدار : 10 الجمعة , ذو الحجة, 1438

الکلمات المفتاحية: OMP, MP, L1-Norm,

ملخص المقالة :

In this paper, at first, compressive sensing theory involves introducing measurement matrices to dedicate the signal dimension and so sensing cost reduction, and sparse domain to examine the conditions for the possibility of signal recovering, are explained. In addition, three well known recovery algorithms called Matching Pursuit (MP), Orthogonal Matching Pursuit (OMP), and L1-Norm are briefly introduced. Then, the performance of three mentioned recovery algorithms are compared with respect to the mean square error (MSE) and the result images quality. For this purpose, Gaussian and Bernoulli as the measurement matrices are used, where Haar and Fourier as sparse domains are applied.