Empirical Recovery Performance Of Fourier Based Deterministic Compressed Sensing
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Empirical Recovery Performance of Fourier-based Deterministic Compressed Sensing
In this thesis, a Fourier-based deterministic sensing matrix is analyzed and applied for deterministic compressed sensing. Based on the construction of this sensing matrix, we deliberately make experiments to compare the recovery performance of Fourier-based deterministic sensing matrix to that of chirp sensing codes and random partial Fourier sensing matrices in terms of empirical recovery performance in noiseless and noisy scenarios. In image reconstruction, the original image can be sparsified by Haar wavelets and then the largest coefficients of the image have been kept for the image reconstructions. Then, Fourier-based deterministic sensing matrices have been applied to the sparsified image to compare the recovery performance with that of random partial Fourier and chirp sensing matrices. Exploiting the structure of the Fourier-based deterministic sensing matrix, the compressive sampling matching pursuit (CoSaMP) algorithm with an efficient fast Fourier transform (FFT) technique is applied for signal and image reconstruction. Finally, experimental results show that the Fourier-based deterministic sensing matrices, together with the CoSaMP greedy recovery algorithm, can empirically guarantee the sparse signal and image recovery with high reliability."-- from abstract.
Sequences and Their Applications -- SETA 2012
This book constitutes the refereed proceedings of the 7th International Conference on Sequences and Their Applications, SETA 2012, held in Waterloo, Canada, in June 2012. The 28 full papers presented together with 2 invited papers in this volume were carefully reviewed and selected from 48 submissions. The papers are grouped in topical sections on perfect sequences; finite fields; boolean functions; Golomb 80th birthday session; linear complexity; frequency hopping; correlation of sequences; bounds on sequences, cryptography; aperiodic correlation; and Walsh transform.