Influence of Beam Distribution on the Quality of Compressed Sensing-Based THz Imaging

We demonstrate terahertz single-pixel imaging is improved by using a photomodulator based on silicon passivated with SiO 2 . By exploring various SiO 2 thicknesses, we show that the modulation factor of the as-fabricated terahertz photomodulator can reach 0.9, three times that based on bare silicon. This improvement originates from chemical passivation, as well as anti-reflection. Single-pixel imaging experiments based on the compressed sensing method show that reconstructed images adopting the new photomodulator have better quality than the conventional terahertz modulator based on bare silicon. Since the passivation process is routine and low cost, we expect this work will reduce the cost of terahertz photomodulator and single-pixel THz imaging, and advance their applications.

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Reconstruction of Slice Image from Solid Rocket Motor Skiagrams Based on Compressed Sensing Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.321-324.1035 ◽

2013 ◽

Vol 321-324 ◽

pp. 1035-1040

Author(s):

Zhi Gao Xu ◽

Chao Ning ◽

Jing Ma ◽

Xiang Bin Li

Keyword(s):

Compressed Sensing ◽

Rocket Motor ◽

Reconstructed Image ◽

Solid Rocket Motor ◽

Sensing Technology ◽

Radiographic Interpretation ◽

Solid Rocket ◽

Slice Image ◽

Algebraic Reconstruction

A reconstruction program of slice image based on SolidRocket Motor (SRM) skiagrams is put forward to overcome the deficiency of artificial radiographic interpretation. The algebraic reconstruction algorithmbased on compressed sensing technology is designed. The influence of radiographic interval angle and skiagram sizes on reconstructed slice image is studied. Radiographic interval angle has a great impact on the quality of the reconstructed image. Slender defects are not sensitive to changes in the length of the skiagram, but circular defects are sensitive to changes in the length of the skiagram. The reconstruction tests of model SRM skiagrams show that the sizes and locations of the debonded defects can be easily ascertained and the efficiency of radiographic interpretation can be greatly improved.

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Adaptive Dual Conjudate Gradient Projection Algorithm for Compressed Sensing Image Reconstruction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.347-350.2600 ◽

2013 ◽

Vol 347-350 ◽

pp. 2600-2604

Author(s):

Hai Xia Yan ◽

Yan Jun Liu

Keyword(s):

Image Reconstruction ◽

Compressed Sensing ◽

Gradient Projection ◽

Projection Algorithm ◽

Sparse Reconstruction ◽

Reconstruction Method ◽

Reconstruction Accuracy ◽

Reconstruction Quality ◽

Dual Gradient

In order to improve the quality of noise signals reconstruction method, an algorithm of adaptive dual gradient projection for sparse reconstruction of compressed sensing theory is proposed. In ADGPSR algorithm, the pursuit direction is updated in two conjudate directions, the better original signals estimated value is computed by conjudate coefficient. Thus the reconstruction quality is improved. Experiment results show that, compared with the GPSR algorithm, the ADGPSR algorithm improves the signals reconstruction accuracy, improves PSNR of reconstruction signals, and exhibits higher robustness under different noise intensities.

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Compressed Sensing Research on Barn Temperature

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.461.160 ◽

2012 ◽

Vol 461 ◽

pp. 160-163

Author(s):

Hong Liang Fu ◽

Hua Wei Tao ◽

Zheng Luo

Keyword(s):

Compressed Sensing ◽

Reconstruction Error ◽

Information Storage ◽

Storage Space ◽

Stored Grain ◽

Wrong Decision ◽

Critical Data ◽

Reconstructed Signal ◽

Feedback Error

That compressed sensing is used in online monitoring of stored grain information could reduce the mass of information storage space and transmission bandwidth. However, due to the question that compressed sensing reconstructed error may cause decision-end to make wrong decision, a limited feedback error controlling method is proposed, wrong decision-making caused by reconstruction error is solved through feedback a small number of critical data. Numerical experiments on barn temperature shows that this method, on the basis of costing a small amount of compression ratio, can effectively improve the quality of reconstructed signal.

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Image-Adaptive GAN Based Reconstruction

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i04.5708 ◽

2020 ◽

Vol 34 (04) ◽

pp. 3121-3129 ◽

Cited By ~ 2

Author(s):

Shady Abu Hussein ◽

Tom Tirer ◽

Raja Giryes

Keyword(s):

Inverse Problems ◽

Compressed Sensing ◽

Super Resolution ◽

Generative Models ◽

Generative Adversarial Networks ◽

Adversarial Networks ◽

Image Super Resolution ◽

Human Faces

In the recent years, there has been a significant improvement in the quality of samples produced by (deep) generative models such as variational auto-encoders and generative adversarial networks. However, the representation capabilities of these methods still do not capture the full distribution for complex classes of images, such as human faces. This deficiency has been clearly observed in previous works that use pre-trained generative models to solve imaging inverse problems. In this paper, we suggest to mitigate the limited representation capabilities of generators by making them image-adaptive and enforcing compliance of the restoration with the observations via back-projections. We empirically demonstrate the advantages of our proposed approach for image super-resolution and compressed sensing.

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Multi-Stage Encoding Scheme for Multiple Audio Objects Using Compressed Sensing

Cybernetics and Information Technologies ◽

10.1515/cait-2015-0074 ◽

2015 ◽

Vol 15 (6) ◽

pp. 135-146

Author(s):

Ziyu Yang ◽

Maoshen Jia ◽

Wenbei Wang ◽

Jiaming Zhang

Keyword(s):

Compressed Sensing ◽

Perceptual Quality ◽

Active Object ◽

Encoding Scheme ◽

Time Frequency ◽

Multi Stage ◽

Object Based

Abstract Object-based audio techniques have become common since they provide the flexibility for personalized rendering. In this paper a multi-stage encoding scheme for multiple audio objects is proposed. The scheme is based on intra-object sparsity. In the encoding phase the dominant Time Frequency (TF) instants of all active object signals are extracted and divided into several stages to form the multistage observation signals for transmission. In the decoding phase the preserved TF instants are recovered via Compressed Sensing (CS) technique, and further used for reconstructing the audio objects. The evaluations validated that the proposed encoding scheme can achieve scalable transmission while maintaining perceptual quality of each audio object.

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