A specific measurement matrix in compressive imaging system

2011 ◽  
Author(s):  
Fen Wang ◽  
Ping Wei ◽  
Jun Ke
Keyword(s):  
Imaging System ◽  
Measurement Matrix ◽  
Compressive Imaging ◽  
Specific Measurement

scholarly journals Measurement Matrix Construction for Large-area Single Photon Compressive Imaging

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 474
Author(s):  
Hui Wang ◽  
Qiurong Yan ◽  
Bing Li ◽  
Chenglong Yuan ◽  
Yuhao Wang
Keyword(s):  
Image Reconstruction ◽  
Imaging System ◽  
Single Photon ◽  
Photon Counting ◽  
Compressive Sampling ◽  
Measurement Matrix ◽  
Compressive Imaging ◽  
Sampling Ratio ◽  
Reconstruction Quality ◽  
M Sequence

We have developed a single photon compressive imaging system based on single photon counting technology and compressed sensing theory, using a photomultiplier tube (PMT) photon counting head as the bucket detector. This system can realize ultra-weak light imaging with the imaging area up to the entire digital micromirror device (DMD) working region. The measurement matrix in this system is required to be binary due to the two working states of the micromirror corresponding to two controlled elements. And it has a great impact on the performance of the imaging system, because it involves modulation of the optical signal and image reconstruction. Three kinds of binary matrix including sparse binary random matrix, m sequence matrix and true random number matrix are constructed. The properties of these matrices are analyzed theoretically with the uncertainty principle. The parameters of measurement matrix including sparsity ratio, compressive sampling ratio and reconstruction time are verified in the experimental system. The experimental results show that, the increase of sparsity ratio and compressive sampling ratio can improve the reconstruction quality. However, when the increase is up to a certain value, the reconstruction quality tends to be saturated. Compared to the other two types of measurement matrices, the m sequence matrix has better performance in image reconstruction.

scholarly journals Modeling and image quality enhancement for dynamic compressive imaging system

2021 ◽  
Vol 15 ◽  
pp. 174830262110080
Author(s):  
Changjun Zha* ◽  
Qian Zhang* ◽  
Huimin Duan
Keyword(s):  
Image Quality ◽  
Imaging System ◽  
Quality Enhancement ◽  
Target Image ◽  
Compressive Imaging ◽  
Image Quality Enhancement ◽  
System A ◽  
Single Column ◽  
The Impact

Traditional single-pixel imaging systems are aimed mainly at relatively static or slowly changing targets. When there is relative motion between the imaging system and the target, sizable deviations between the measurement values and the real values can occur and result in poor image quality of the reconstructed target. To solve this problem, a novel dynamic compressive imaging system is proposed. In this system, a single-column digital micro-mirror device is used to modulate the target image, and the compressive measurement values are obtained for each column of the image. Based on analysis of the measurement values, a new recovery model of dynamic compressive imaging is given. Differing from traditional reconstruction results, the measurement values of any column of vectors in the target image can be used to reconstruct the vectors of two adjacent columns at the same time. Contingent upon characteristics of the results, a method of image quality enhancement based on an overlapping average algorithm is proposed. Simulation experiments and analysis show that the proposed dynamic compressive imaging can effectively reconstruct the target image; and that when the moving speed of the system changes within a certain range, the system reconstructs a better original image. The system overcomes the impact of dynamically changing speeds, and affords significantly better performance than traditional compressive imaging.

scholarly journals Block-wise motion detection using compressive imaging system

2011 ◽  
Vol 284 (5) ◽  
pp. 1170-1180 ◽  
Author(s):  
Jun Ke ◽  
Amit Ashok ◽  
Mark A. Neifeld
Keyword(s):  
Motion Detection ◽  
Imaging System ◽  
Compressive Imaging

A Terahertz Compressive Imaging Method Based on Single Detector of Randomly Moving Template

2013 ◽  
Vol 756-759 ◽  
pp. 3785-3788
Author(s):  
Sai Qi Shang ◽  
Min Gang Wang ◽  
Wei Li ◽  
Yao Yang
Keyword(s):  
Compressed Sensing ◽  
Imaging System ◽  
Matching Pursuit ◽  
Terahertz Imaging ◽  
Compressive Sampling ◽  
Imaging Method ◽  
Original Image ◽  
Reconstruction Algorithms ◽  
Compressive Imaging ◽  
Single Detector

Expensiveness and lack of N-pixels sensor affect the application of terahertz imaging. New compressed sensing theory recently achieved a major breakthrough in the field of signal codec, making it possible to recover the original image by using the measured values, which have much smaller number than the pixels in the image. In this paper, by comparing the measurement matrices based on different reconstruction algorithms, such as Orthogonal Matching Pursuit, Compressive Sampling Matching Pursuit and Minimum L_1 Norm algorithms, we proposed a terahertz imaging method based on single detector of randomly moving measurement matrices, designed the mobile random templates and an automatically template changing mechanism, constructed a single detector imaging system, and completed the single terahertz detector imaging experiments.

scholarly journals Characterization of a compressive imaging system using laboratory and natural light scenes

2013 ◽  
Vol 52 (19) ◽  
pp. 4515 ◽  
Author(s):  
Stephen J. Olivas ◽  
Yaron Rachlin ◽  
Lydia Gu ◽  
Brian Gardiner ◽  
Robin Dawson ◽  
...  
Keyword(s):  
Imaging System ◽  
Natural Light ◽  
Compressive Imaging

scholarly journals Adaptive Single Photon Compressed Imaging Based on Constructing a Smart Threshold Matrix

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3449
Author(s):  
Wentao Shangguan ◽  
Qiurong Yan ◽  
Hui Wang ◽  
Chenglong Yuan ◽  
Bing Li ◽  
...  
Keyword(s):  
Random Matrix ◽  
Adaptive Sampling ◽  
Imaging System ◽  
Single Photon ◽  
A Priori ◽  
Photon Counting ◽  
Reconstruction Algorithms ◽  
Measurement Matrix ◽  
Adaptive Imaging ◽  
Adaptive Measurement

We demonstrate a single-photon compressed imaging system based on single photon counting technology and compressed sensing theory. In order to cut down the measurement times and shorten the imaging time, a fast and efficient adaptive sampling method, suited for single-photon compressed imaging, is proposed. First, the pre-measured rough images are transformed into sparse bases as a priori information. Then a smart threshold matrix is designed by using large sparse coefficients of the rough image in sparse bases. The adaptive measurement matrix is obtained by modifying the original Gaussian random matrix with the specially designed threshold matrix. Building the adaptive measurement matrix requires only one level of sparse representation, which means that adaptive imaging can be achieved quickly with very little computation. The experimental results show that the reconstruction effect of the image measured using the adaptive measurement matrix is obviously superior than that of the Gaussian random matrix under different measurement times and different reconstruction algorithms.

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