Compare Performance of Recovery Algorithms MP, OMP, L1-Norm in Compressive Sensing for Different Measurement and Sparse Spaces
سال انتشار: 1396
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 528
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شناسه ملی سند علمی:
JR_SPRE-1-3_003
تاریخ نمایه سازی: 23 تیر 1398
چکیده مقاله:
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.
نویسندگان
Bahareh Davoodi
Electrical Engineering Department South Tehran Branch, Islamic Azad University Tehran, Iran
Sedigheh Ghofrani
Electrical Engineering Department South Tehran Branch, Islamic Azad University Tehran, Iran