Iterative gradient projection algorithm for two-dimensional compressive sensing sparse image reconstruction

Chen, Gao; Li, Defang; Zhang, Jiashu

doi:10.1016/j.sigpro.2014.03.039

Cited by 56 publications

(25 citation statements)

References 37 publications

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“…Other related reconstruction algorithms, including orthogonal matching pursuits (OMP) [18], iterative gradient projection algorithm [19], and iterative hard thresholding (IHT) algorithm [20], can also be used to solve the l 1 -norm minimization problem effectively.…”

Section: Traditional Compressed Sensingmentioning

confidence: 99%

Optimal Permutation Based Block Compressed Sensing for Image Compression Applications

Cao

Gong

Zhang³

et al. 2018

IEICE Trans. Inf. & Syst.

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SUMMARYBlock compressed sensing (CS) with optimal permutation is a promising method to improve sampling efficiency in CS-based image compression. However, the existing optimal permutation scheme brings a large amount of extra data to encode the permutation information because it needs to know the permutation information to accomplish signal reconstruction. When the extra data is taken into consideration, the improvement in sampling efficiency of this method is limited. In order to solve this problem, a new optimal permutation strategy for block CS (BCS) is proposed. Based on the proposed permutation strategy, an improved optimal permutation based BCS method called BCS-NOP (BCS with new optimal permutation) is proposed in this paper. Simulation results show that the proposed approach reduces the amount of extra data to encode the permutation information significantly and thereby improves the sampling efficiency compared with the existing optimal permutation based BCS approach.

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Section: Traditional Compressed Sensingmentioning

confidence: 99%

Optimal Permutation Based Block Compressed Sensing for Image Compression Applications

Cao

Gong

Zhang³

et al. 2018

IEICE Trans. Inf. & Syst.

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“…Also, a 2D CS image reconstruction algorithm based on iterative gradient projection is derived in [25].…”

Section: Introductionmentioning

confidence: 99%

Efficient two-dimensional compressive sensing in MIMO radar

Shahbazi

Abbasfar

Jabbarian-Jahromi

2017

EURASIP J. Adv. Signal Process.

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Compressive sensing (CS) has been a way to lower sampling rate leading to data reduction for processing in multiple-input multiple-output (MIMO) radar systems. In this paper, we further reduce the computational complexity of a pulse-Doppler collocated MIMO radar by introducing a two-dimensional (2D) compressive sensing. To do so, we first introduce a new 2D formulation for the compressed received signals and then we propose a new measurement matrix design for our 2D compressive sensing model that is based on minimizing the coherence of sensing matrix using gradient descent algorithm. The simulation results show that our proposed 2D measurement matrix design using gradient decent algorithm (2D-MMDGD) has much lower computational complexity compared to one-dimensional (1D) methods while having better performance in comparison with conventional methods such as Gaussian random measurement matrix.

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“…To address these drawbacks, some 2D reconstruction algorithms that directly leverage the matrix structure of 2D sparse signals have been proposed recently [16][17][18][19][20][21], and some have been used in radar imaging systems. For example, a fast reconstruction algorithm, called two-dimensional smoothed L0 norm (2D-SL0) algorithm, has been proposed to reduce computational complexity and economize on the memory required by directly utilizes the matrix structure to recover the 2D sparse signals and is designed [20], but the reconstruction quality of the natural image is poor.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, an iterative gradient projection algorithm for 2D sparse image reconstruction has been proposed, in which the sparse solution is searched iteratively from the 2D solution space and then updated by gradient descent of the total variation. It recovers the natural image perfectly, being conducive to reduction in both computational complexity and memory requirements for the measurement matrix [18]; however, the algorithm suffers from the limitation that the sparse signals must be square.…”

Section: Introductionmentioning

confidence: 99%

2D Normalized Iterative Hard Thresholding Algorithm for Fast Compressive Radar Imaging

Yang

Wang

et al. 2017

Remote Sensing

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Abstract:Compressive radar imaging has attracted considerable attention because it substantially reduces imaging time through directly compressive sampling. However, a problem that must be addressed for compressive radar imaging systems is the high computational complexity of reconstruction of sparse signals. In this paper, a novel algorithm, called two-dimensional (2D) normalized iterative hard thresholding (NIHT) or 2D-NIHT algorithm, is proposed to directly reconstruct radar images in the matrix domain. The reconstruction performance of 2D-NIHT algorithm was validated by an experiment on recovering a synthetic 2D sparse signal, and the superiority of the 2D-NIHT algorithm to the NIHT algorithm was demonstrated by a comprehensive comparison of its reconstruction performance. Moreover, to be used in compressive radar imaging systems, a 2D sampling model was also proposed to compress the range and azimuth data simultaneously. The practical application of the 2D-NIHT algorithm in radar systems was validated by recovering two radar scenes with noise at different signal-to-noise ratios, and the results showed that the 2D-NIHT algorithm could reconstruct radar scenes with a high probability of exact recovery in the matrix domain. In addition, the reconstruction performance of the 2D-NIHT algorithm was compared with four existing efficient reconstruction algorithms using the two radar scenes, and the results illustrated that, compared to the other algorithms, the 2D-NIHT algorithm could dramatically reduce the computational complexity in signal reconstruction and successfully reconstruct 2D sparse images with a high probability of exact recovery.

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Iterative gradient projection algorithm for two-dimensional compressive sensing sparse image reconstruction

Cited by 56 publications

References 37 publications

Optimal Permutation Based Block Compressed Sensing for Image Compression Applications

Optimal Permutation Based Block Compressed Sensing for Image Compression Applications

Efficient two-dimensional compressive sensing in MIMO radar

2D Normalized Iterative Hard Thresholding Algorithm for Fast Compressive Radar Imaging

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