Comparison of reconstruction accuracy of sparse recovery algorithms for gapped RCS data

2015 ◽  
Vol 57 (5) ◽  
pp. 1249-1255 ◽  
Author(s):  
Ji-Hoon Bae ◽  
Sang-Hong Park ◽  
Byung-Soo Kang ◽  
Kyung-Tae Kim ◽  
Eunjung Yang
Keyword(s):  
Sparse Recovery ◽  
Reconstruction Accuracy ◽  
Recovery Algorithms

Classification of ISAR images using sparse recovery algorithms

2014 ◽  
Author(s):  
Seung-Jae Lee ◽  
Ji-Hoon Bae ◽  
Byung-Soo Kang ◽  
Kyung-Tae Kim ◽  
Eun-Jung Yang
Keyword(s):  
Sparse Recovery ◽  
Recovery Algorithms

scholarly journals A Fast Sparse Recovery Algorithm for Compressed Sensing Using Approximate l0 Norm and Modified Newton Method

Materials ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1227 ◽  
Author(s):  
Dingfei Jin ◽  
Yue Yang ◽  
Tao Ge ◽  
Daole Wu
Keyword(s):  
Compressed Sensing ◽  
Newton Method ◽  
Simulation Experiment ◽  
Sparse Recovery ◽  
Signal Recovery ◽  
Recovery Efficiency ◽  
Recovery Algorithm ◽  
Modified Newton Method ◽  
Computer Simulation Experiment ◽  
Recovery Algorithms

In this paper, we propose a fast sparse recovery algorithm based on the approximate l0 norm (FAL0), which is helpful in improving the practicability of the compressed sensing theory. We adopt a simple function that is continuous and differentiable to approximate the l0 norm. With the aim of minimizing the l0 norm, we derive a sparse recovery algorithm using the modified Newton method. In addition, we neglect the zero elements in the process of computing, which greatly reduces the amount of computation. In a computer simulation experiment, we test the image denoising and signal recovery performance of the different sparse recovery algorithms. The results show that the convergence rate of this method is faster, and it achieves nearly the same accuracy as other algorithms, improving the signal recovery efficiency under the same conditions.

scholarly journals Multitrack Compressed Sensing for Faster Hyperspectral Imaging

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5034
Author(s):  
Sharvaj Kubal ◽  
Elizabeth Lee ◽  
Chor Yong Tay ◽  
Derrick Yong
Keyword(s):  
Compressed Sensing ◽  
Hyperspectral Imaging ◽  
Sparse Recovery ◽  
Reconstruction Accuracy ◽  
Recovery Algorithm ◽  
Additional Information ◽  
Different Types ◽  
Block Compressed Sensing ◽  
Speed And Accuracy ◽  
Reconstruction Time

Hyperspectral imaging (HSI) provides additional information compared to regular color imaging, making it valuable in areas such as biomedicine, materials inspection and food safety. However, HSI is challenging because of the large amount of data and long measurement times involved. Compressed sensing (CS) approaches to HSI address this, albeit subject to tradeoffs between image reconstruction accuracy, time and generalizability to different types of scenes. Here, we develop improved CS approaches for HSI, based on parallelized multitrack acquisition of multiple spectra per shot. The multitrack architecture can be paired up with either of the two compatible CS algorithms developed here: (1) a sparse recovery algorithm based on block compressed sensing and (2) an adaptive CS algorithm based on sampling in the wavelet domain. As a result, the measurement speed can be drastically increased while maintaining reconstruction speed and accuracy. The methods were validated computationally both in noiseless as well as noisy simulated measurements. Multitrack adaptive CS has a ∼10 times shorter measurement plus reconstruction time as compared to full sampling HSI without compromising reconstruction accuracy across the sample images tested. Multitrack non-adaptive CS (sparse recovery) is most robust against Poisson noise at the expense of longer reconstruction times.

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