scholarly journals Optimization Methods of Compressively Sensed Image Reconstruction Based on Single-Pixel Imaging

2020 ◽  
Vol 10 (9) ◽  
pp. 3288 ◽  
Author(s):  
Ziran Wei ◽  
Jianlin Zhang ◽  
Zhiyong Xu ◽  
Yong Liu
Keyword(s):  
Image Reconstruction ◽  
Compressive Sensing ◽  
Imaging System ◽  
Optimization Methods ◽  
Superior Performance ◽  
Gram Matrix ◽  
Constraint Matrix ◽  
Single Pixel ◽  
Sensing Matrices

According to the theory of compressive sensing, a single-pixel imaging system was built in our laboratory, and imaging scenes are successfully reconstructed by single-pixel imaging, but the quality of reconstructed images in traditional methods cannot meet the demands of further engineering applications. In order to improve the imaging accuracy of our single-pixel camera, some optimization methods of key technologies in compressive sensing are proposed in this paper. First, in terms of sparse signal decomposition, based on traditional discrete wavelet transform and the characteristics of coefficients distribution in wavelet domain, a constraint condition of the exponential decay is proposed and a corresponding constraint matrix is designed to optimize the original wavelet decomposition basis. Second, for the construction of deterministic binary sensing matrices in the single-pixel camera, on the basis of a Gram matrix, a new algorithm model and a new method of initializing a compressed sensing measurement matrix are proposed to optimize the traditional binary sensing matrices via mutual coherence minimization. The gradient projection-based algorithm is used to solve the new mathematical model and train deterministic binary sensing measurement matrices with better performance. Third, the proposed optimization methods are applied to our single-pixel imaging system for optimizing the existing imaging methods. Compared with the conventional methods of single-pixel imaging, the accuracy of image reconstruction and the quality of single-pixel imaging have been significantly improved by our methods. The superior performance of our proposed methods has been fully tested and the effectiveness has also been demonstrated by numerical simulation experiments and practical imaging experiments.

A compressive sensing single pixel imaging system using in-fiber grating

2021 ◽  
Author(s):  
Guoqing Wang ◽  
Liyang Shao ◽  
Dongrui Xiao ◽  
Fang Zhao ◽  
Ping Shum ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Imaging System ◽  
Fiber Grating ◽  
Single Pixel

scholarly journals Measurement Matrix Optimization via Mutual Coherence Minimization for Compressively Sensed Signals Reconstruction

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Ziran Wei ◽  
Jianlin Zhang ◽  
Zhiyong Xu ◽  
Yong Liu ◽  
Krzysztof Okarma
Keyword(s):  
Optimization Methods ◽  
Superior Performance ◽  
Gram Matrix ◽  
Sparse Signals ◽  
Two Dimensional ◽  
Measurement Matrix ◽  
Mutual Coherence ◽  
One Dimensional ◽  
Two Dimensional Image ◽  
Matrix Optimization

For signals reconstruction based on compressive sensing, to reconstruct signals of higher accuracy with lower compression rates, it is required that there is a smaller mutual coherence between the measurement matrix and the sparsifying matrix. Mutual coherence between the measurement matrix and sparsifying matrix can be expressed indirectly by the property of the Gram matrix. On the basis of the Gram matrix, a new optimization algorithm of acquiring a measurement matrix has been proposed in this paper. Firstly, a new mathematical model is designed and a new method of initializing measurement matrix is adopted to optimize the measurement matrix. Then, the loss function of the new algorithm model is solved by the gradient projection-based method of Gram matrix approximating an identity matrix. Finally, the optimized measurement matrix is generated by minimizing mutual coherence between measurement matrix and sparsifying matrix. Compared with the conventional measurement matrices and the traditional optimization methods, the proposed new algorithm effectively improves the performance of optimized measurement matrices in reconstructing one-dimensional sparse signals and two-dimensional image signals that are not sparse. The superior performance of the proposed method in this paper has been fully tested and verified by a large number of experiments.

scholarly journals Dual Optical Path Based Adaptive Compressive Sensing Imaging System

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6200
Author(s):  
Hongliang Li ◽  
Ke Lu ◽  
Jian Xue ◽  
Feng Dai ◽  
Yongdong Zhang
Keyword(s):  
Compressive Sensing ◽  
Deep Neural Networks ◽  
Imaging System ◽  
Effective Theory ◽  
Optical Imaging System ◽  
The Difference ◽  
Digital Micromirror Devices ◽  
Bidirectional Reflection ◽  
Reflection Characteristics

Compressive Sensing (CS) has proved to be an effective theory in the field of image acquisition. However, in order to distinguish the difference between the measurement matrices, the CS imaging system needs to have a higher signal sampling accuracy. At the same time, affected by the noise of the light path and the circuit, the measurements finally obtained are noisy, which directly affects the imaging quality. We propose a dual-optical imaging system that uses the bidirectional reflection characteristics of digital micromirror devices (DMD) to simultaneously acquire CS measurements and images under the same viewing angle. Since deep neural networks have powerful modeling capabilities, we trained the filter network and the reconstruction network separately. The filter network is used to filter the noise in the measurements, and the reconstruction network is used to reconstruct the CS image. Experiments have proved that the method we proposed can filter the noise in the sampling process of the CS system, and can significantly improve the quality of image reconstruction under a variety of algorithms.

scholarly journals Compressive Sensing-Based SAR Image Reconstruction from Sparse Radar Sensor Data Acquisition in Automotive FMCW Radar System

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7283
Author(s):  
Seongwook Lee ◽  
Yunho Jung ◽  
Myeongjin Lee ◽  
Wookyung Lee
Keyword(s):  
Fourier Transform ◽  
Image Reconstruction ◽  
Data Acquisition ◽  
Compressive Sensing ◽  
Sensor Data ◽  
Sar Image ◽  
Radar Sensor ◽  
Sar Images ◽  
Recovery Method

In this paper, we propose a method for reconstructing synthetic aperture radar (SAR) images by applying a compressive sensing (CS) technique to sparsely acquired radar sensor data. In general, SAR image reconstruction algorithms require radar sensor data acquired at regular spatial intervals. However, when the speed of the radar-equipped platform is not constant, it is difficult to consistently perform regular data acquisitions. Therefore, we used the CS-based signal recovery method to efficiently reconstruct SAR images even when regular data acquisition was not performed. In the proposed method, we used the l1-norm minimization to overcome the non-uniform data acquisition problem, which replaced the Fourier transform and inverse Fourier transform in the conventional SAR image reconstruction method. In addition, to reduce the phase distortion of the recovered signal, the proposed method was applied to each of the in-phase and quadrature components of the acquired radar sensor data. To evaluate the performance of the proposed method, we conducted experiments using an automotive frequency-modulated continuous wave radar sensor. Then, the quality of the SAR image reconstructed with data acquired at regular intervals was compared with the quality of images reconstructed with data acquired at non-uniform intervals. Using the proposed method, even if only 70% of the regularly acquired radar sensor data was used, a SAR image having a correlation of 0.83 could be reconstructed.

A Compressive Sensing Single Pixel Imaging System Using Cascaded Mach-Zehnder Interference Structure

2021 ◽  
Author(s):  
Guoqing Wang ◽  
Liyang Shao ◽  
Dongrui Xiao ◽  
Fang Zhao ◽  
Ping Shum ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Imaging System ◽  
Interference Structure ◽  
Single Pixel
Sign in / Sign up

Export Citation Format

Share Document