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compressive-sampling
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Weighted sparse signal decomposition
, Article ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings ; 2012 , Pages 3425-3428 ; 15206149 (ISSN) ; 9781467300469 (ISBN) ; Mehrdad, B ; Giannakis, G. B ; Sharif University of Technology
IEEE
2012
Abstract
Standard sparse decomposition (with applications in many different areas including compressive sampling) amounts to finding the minimum ℓ 0-norm solution of an underdetermined system of linear equations. In this decomposition, all atoms are treated 'uniformly' for being included or not in the decomposition. However, one may wish to weigh more or less certain atoms, or, assign higher costs to some other atoms to be included in the decomposition. This can happen for example when there is prior information available on each atom. This motivates generalizing the notion of minimal ℓ 0-norm solution to that of minimal weighted ℓ 0-norm solution. On the other hand, relaxing weighted ℓ 0-norm via...
On the use of compressive sensing for image enhancement
, Article Proceedings - 2016 UKSim-AMSS 18th International Conference on Computer Modelling and Simulation, UKSim 2016, 6 April 2016 through 8 April 2016 ; 2016 , Pages 167-171 ; 9781509008889 (ISBN) ; Ghorshi, S ; Khoshnevis, S. A ; Pourebrahim, M ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2016
Abstract
Compressed Sensing (CS), as a new rapidly growing research field, promises to effectively recover a sparse signal at the rate of below Nyquist rate. This revolutionary technology strongly relies on the sparsity of the signal and incoherency between sensing basis and representation basis. Exact recovery of a sparse signal will be occurred in a situation that the signal of interest sensed randomly and the measurements are also taken based on sparsity level and log factor of the signal dimension. In this paper, compressed sensing method is proposed to reduce the noise and reconstruct the image signal. Noise reduction and image reconstruction are formulated in the theoretical framework of...
Two dimensional compressive classifier for sparse images
, Article 2009 IEEE International Conference on Image Processing, ICIP 2009, Cairo, 7 November 2009 through 10 November 2009 ; 2009 , Pages 2137-2140 ; 15224880 (ISSN) ; 9781424456543 (ISBN) ; Abrishami Moghaddam, H ; Babaie Zadeh, M ; Sharif University of Technology
Abstract
The theory of compressive sampling involves making random linear projections of a signal. Provided signal is sparse in some basis, small number of such measurements preserves the information in the signal, with high probability. Following the success in signal reconstruction, compressive framework has recently proved useful in classification. In this paper, conventional random projection scheme is first extended to the image domain and the key notion of concentration of measure is studied. Findings are then employed to develop a 2D compressive classifier (2DCC) for sparse images. Finally, theoretical results are validated within a realistic experimental framework. ©2009 IEEE
Applications of sparse signal processing
, Article 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016, 7 December 2016 through 9 December 2016 ; 2017 , Pages 1349-1353 ; 9781509045457 (ISBN) ; Marvasti, F ; IEEE Signal Processing Society; The Institute of Electrical and Electronics Engineers ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2017
Abstract
Sparse signal processing has found various applications in different research areas where the sparsity of the signal of interest plays a significant role in addressing their ill-posedness. In this invited paper, we give a brief review of a number of such applications in inverse scattering of microwave medical imaging, compressed video sensing, and missing sample recovery based on sparsity. Moreover, some of our recent results on these areas have been reported which confirms the fact that leveraging the sparsity prior of the underlying signal can improve different processing tasks in various problems. © 2016 IEEE
Deterministic Sensing Matrix Design in Compressive Sensing
, M.Sc. Thesis Sharif University of Technology ; Amini, Arash (Supervisor)
Abstract
Sampling and recovery of a signal is one of the crucial issues in communication systems. In conventional methods, proper recovery is achieved by sampling the signal at the Nyquist rate, which is twice the signal bandwidth. There have been attempts on reducing the required sampling rate, all of which end in rates equal to a factor of the signal bandwidth. Assuming the sparse nature of the signal in hand, which is a reasonable assumption in many real world scenarios, the theory of Compressed Sensing suggests a sampling rate much less than the Nyquist rate. Designing suitable sensing matrices and efficient recovery of the signal from its samples are the two major challenges of Compressed...
Speech Enhancement Based upon Compressed Sensing
, M.Sc. Thesis Sharif University of Technology ; Ghorshi, Alireza (Supervisor)
Abstract
This thesis proposes a novel method for enhancing the speech signal based on compressed sensing. Compressed sensing, as a new rapidly growing research field, promises to effectively recover a sparse signal at the rate of below Nyquist rate. This revolutionary technology strongly relies on the sparsity of the signal and incoherency between sensing basis and representation basis. Exact recovery of a sparse signal will be occurred in a situation that the signal of interest sensed randomly and the measurements are also taken based on sparsity level and log factor of the signal dimension.
In this research, compressed sensing method is proposed to reconstruct speech signal and for noise...
In this research, compressed sensing method is proposed to reconstruct speech signal and for noise...
Applying Compressive Sensing Techniques for Image Enhancement
, M.Sc. Thesis Sharif University of Technology ; Ghorshi, Mohammad Ali (Supervisor)
Abstract
This thesis proposes a novel method for enhancing the image signal based on compressed sensing. Compressed sensing, as a new rapidly growing research field, promises to effectively recover a sparse signal at the rate of below Nyquist rate. This revolutionary technology strongly relies on the sparsity of the signal and incoherency between sensing basis and representation basis. Exact recovery of a sparse signal will be occurred in a situation that the signal of interest sensed randomly and the measurements are also taken based on sparsity level and log factor of the signal dimension. In this research, compressed sensing method is proposed to reduce the noise and reconstruct the image signal....
On the achievability of Cramér-Rao bound in noisy compressed sensing
, Article IEEE Transactions on Signal Processing ; Volume 60, Issue 1 , 2012 , Pages 518-526 ; 1053587X (ISSN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
2012
Abstract
Recently, it has been proved in Babadi [B. Babadi, N. Kalouptsidis, and V. Tarokh, "Asymptotic achievability of the Cramér-Rao bound for noisy compressive sampling", IEEE Trans. Signal Process., vol. 57, no. 3, pp. 1233-1236, 2009] that in noisy compressed sensing, a joint typical estimator can asymptotically achieve the Cramér-Rao lower bound of the problem. To prove this result, Babadi used a lemma, which is provided in Akçakaya and Tarokh [M. Akçakaya and V. Trarokh, "Shannon theoretic limits on noisy compressive sampling", IEEE Trans. Inf. Theory, vol. 56, no. 1, pp. 492-504, 2010] that comprises the main building block of the proof. This lemma is based on the assumption of Gaussianity...
Improved CT image reconstruction through partial Fourier sampling
, Article Scientia Iranica ; Volume 23, Issue 6 , 2016 , Pages 2908-2916 ; 10263098 (ISSN) ; Kavehvash, Z ; Shabany, M ; Sharif University of Technology
Sharif University of Technology
2016
Abstract
A novel CT imaging structure based on Compressive Sensing (CS) is proposed. The main goal is to mitigate the CT imaging time and, thus, X-ray radiation dosage without compromising the image quality. The utilized compressive sensing approach is based on radial Fourier sampling. Thanks to the intrinsic relation between captured radon samples in a CT imaging process and the radial Fourier samples, partial Fourier sampling could be implemented systematically. This systematic compressive sampling helps in better control of required conditions such as incoherence and sparsity to guarantee adequate image quality in comparison to previous CS-based CT imaging structures. Simulation results prove the...
Iterative block-sparse recovery method for distributed MIMO radar
, Article 2016 Iran Workshop on Communication and Information Theory, IWCIT 2016, 3 May 2016 through 4 May 2016 ; 2016 ; 9781509019229 (ISBN) ; Azghani, M ; Tayefi, J ; Marvasti, F ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2016
Abstract
In this paper, an iterative method for block-sparse recovery is suggested for target parameters estimation in a distributed MIMO radar system. The random sampling has been used as the sensing scheme in the receivers. The simulation results prove that the proposed method is superior to the other state-of-the-art techniques in the accuracy of the target estimation task. © 2016 IEEE
Two dimensional compressive classifier for sparse images
, Article Proceedings of the 2009 6th International Conference on Computer Graphics, Imaging and Visualization: New Advances and Trends, CGIV2009, 11 August 2009 through 14 August 2009, Tianjin ; 2009 , Pages 402-405 ; 9780769537894 (ISBN) ; Moghaddam, H. A ; Babaie Zadeh, M ; Sharif University of Technology
Abstract
The theory of compressive sampling involves making random linear projections of a signal. Provided signal is sparse in some basis, small number of such measurements preserves the information in the signal, with high probability. Following the success in signal reconstruction, compressive framework has recently proved useful in classification, particularly hypothesis testing. In this paper, conventional random projection scheme is first extended to the image domain and the key notion of concentration of measure is closely studied. Findings are then employed to develop a 2D compressive classifier (2D-CC) for sparse images. Finally, theoretical results are validated within a realistic...
Range-Doppler Map Generation in the Presence of Sparse Clutter for Multistatic Radar
, M.Sc. Thesis Sharif University of Technology ; Karbasi, Mohammad (Supervisor)
Abstract
Multistatic radar has several advantages over monostatic radar (such as better detection), which are due to the use of different viewing angles and the difference in their clutter characteristics. Clutter in many applications (such as marine applications) has the property of being sparse in certain dictionaries. Therefore, the investigation of sparse clutter (such as sea clutter) is of particular importance. It is worth noting that the detection of targets in the vicinity of the sea faces difficulties due to the dynamics of the sea, which causes the Doppler spectrum to change with time and change in space. Considering the fact that the sea clutter is sparse clutter, one of the powerful...
Multihypothesis compressed video sensing technique
, Article IEEE Transactions on Circuits and Systems for Video Technology ; Volume 26, Issue 4 , 2016 , Pages 627-635 ; 10518215 (ISSN) ; Karimi, M ; Marvasti, F ; Sharif University of Technology
Abstract
In this paper, we present a compressive sampling and multihypothesis (MH) reconstruction strategy for video sequences that has a rather simple encoder, while the decoding system is not that complex. We introduce a convex cost function that incorporates the MH technique with the sparsity constraint and the Tikhonov regularization. Consequently, we derive a new iterative algorithm based on these criteria. This algorithm surpasses its counterparts (Elasticnet and Tikhonov) in recovery performance. Besides, it is computationally much faster than Elasticnet and comparable with Tikhonov. Our extensive simulation results confirm these claims