Ultrasonic Block Compressed Sensing Imaging Reconstruction Algorithm Based on Wavelet Sparse Representation

2020 ◽  
Vol 16 (3) ◽  
pp. 262-272
Author(s):  
Guangzhi Dai ◽  
Zhiyong He ◽  
Hongwei Sun
Keyword(s):  
Compressed Sensing ◽  
Sparse Representation ◽  
Linear Array ◽  
Imaging System ◽  
Ultrasonic Imaging ◽  
Large Data ◽  
Reconstruction Algorithm ◽  
Array Transducer ◽  
Total Data ◽  
Block Compressed Sensing

Background: This study is carried out targeting the problem of slow response time and performance degradation of imaging system caused by large data of medical ultrasonic imaging. In view of the advantages of CS, it is applied to medical ultrasonic imaging to solve the above problems. Objective: Under the condition of satisfying the speed of ultrasound imaging, the quality of imaging can be further improved to provide the basis for accurate medical diagnosis. Methods: According to CS theory and the characteristics of the array ultrasonic imaging system, block compressed sensing ultrasonic imaging algorithm is proposed based on wavelet sparse representation. Results: Three kinds of observation matrices have been designed on the basis of the proposed algorithm, which can be selected to reduce the number of the linear array channels and the complexity of the ultrasonic imaging system to some extent. Conclusion: The corresponding simulation program is designed, and the result shows that this algorithm can greatly reduce the total data amount required by imaging and the number of data channels required for linear array transducer to receive data. The imaging effect has been greatly improved compared with that of the spatial frequency domain sparse algorithm.

Industrial Ultrasonic Imaging Based on Compressed Sensing

2012 ◽  
Vol 256-259 ◽  
pp. 2328-2332
Author(s):  
Guang Zhi Dai ◽  
Wei Yi Lin ◽  
Guo Qiang Han
Keyword(s):  
Compressed Sensing ◽  
Imaging System ◽  
Signal Reconstruction ◽  
Ultrasonic Imaging ◽  
Reconstruction Algorithm ◽  
Physical Structure ◽  
Finite Rate ◽  
Array Technology ◽  
Ultrasonic Phased Array ◽  
Finite Rate Of Innovation

Industrial ultrasonic imaging system based on compressed sensing(IUICS),is still lack of available implementation, due to its difficulty in hardware realization.However,thanks to the recent finite rate of innovation and ultrasonic phased array technology,it is possible to apply Compressive Sensing framework to industrial ultrasonic imaging system.In this paper,we propose an available scheme of industrial ultrasonic imaging,which includes the sampling of signal,reconstruction algorithm and its physical structure, based on Compressed Sensing.

scholarly journals Super-resolution reconstruction for a single image based on self-similarity and compressed sensing

2018 ◽  
Vol 12 (3) ◽  
pp. 234-244
Author(s):  
Qiang Yang ◽  
Huajun Wang
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Sparse Representation ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Single Image ◽  
Low Resolution ◽  
Self Similarity ◽  
Resolution Image ◽  
Reconstruction Performance

Super-resolution image reconstruction can achieve favorable feature extraction and image analysis. This study first investigated the image’s self-similarity and constructed high-resolution and low-resolution learning dictionaries; then, based on sparse representation and reconstruction algorithm in compressed sensing theory, super-resolution reconstruction (SRSR) of a single image was realized. The proposed algorithm adopted improved K-SVD algorithm for sample training and learning dictionary construction; additionally, the matching pursuit algorithm was improved for achieving single-image SRSR based on image’s self-similarity and compressed sensing. The experimental results reveal that the proposed reconstruction algorithm shows better visual effect and image quality than the degraded low-resolution image; moreover, compared with the reconstructed images using bilinear interpolation and sparse-representation-based algorithms, the reconstructed image using the proposed algorithm has a higher PSNR value and thus exhibits more favorable super-resolution image reconstruction performance.

scholarly journals Compressed Sensing Techniques for Ultrasonic Imaging of Cargo Containers

2016 ◽  
Author(s):  
José Á. Martínez Lorenzo ◽  
Yuri Álvarez López
Keyword(s):  
Compressed Sensing ◽  
Imaging System ◽  
Sampling Rate ◽  
Ultrasonic Imaging ◽  
Probability Of Detection ◽  
Inspection System ◽  
Metallic Structure ◽  
Acoustic Image ◽  
Cargo Inspection ◽  
Cargo Containers

This contribution presents a compressed sensing (CS)-based ultrasonic imaging system for fast, low-cost inspection of metallic cargo containers. The idea is to detect the footprint of metallic objects within the container that can be used to conceal smuggling goods. This ultrasonic technology can complement currently deployed X-ray-based radiographic systems and millimeter-wave scanners, thus increasing the probability of detection. The proposed hardware consists of an array of acoustic transceivers that is attached to the metallic structure of the metallic cargo container to create a guided acoustic wave. Variations in the thickness of the metallic structure create reflections that can be located by backpropagating the measured reflected wave. Aiming to reduce the number of acoustic transceivers, this contribution evaluates the feasibility of applying CS techniques in the proposed acoustic imaging system. It has been observed that in the majority of the cases, the acoustic images retrieved by the cargo inspection system are sparse, that is, only those image pixels corresponding to discontinuities in the metallic plate (due to gaps, joints, placement of a metallic object on it) are different from zero. Thus, sparsity condition, which is one of the CS requirements, is satisfied for this particular application. A simulation-based example resembling a real case of cargo inspection is considered for validation purposes. A comparison between standard backpropagation and CS for different number of samples is presented, proving that CS is able to recover the acoustic image with as few as 10% of the samples required by Nyquist sampling rate.

scholarly journals Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications

2020 ◽  
Vol 10 (17) ◽  
pp. 5909
Author(s):  
Lixiang Li ◽  
Yuan Fang ◽  
Liwei Liu ◽  
Haipeng Peng ◽  
Jürgen Kurths ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Reconstruction Algorithm ◽  
Transmission Efficiency ◽  
Sampling Theorem ◽  
Reconstruction Algorithms ◽  
Intelligent Networks ◽  
Shannon Sampling Theorem ◽  
Sensing Model ◽  
Block Compressed Sensing ◽  
The Internet Of Things

With the development of intelligent networks such as the Internet of Things, network scales are becoming increasingly larger, and network environments increasingly complex, which brings a great challenge to network communication. The issues of energy-saving, transmission efficiency, and security were gradually highlighted. Compressed sensing (CS) helps to simultaneously solve those three problems in the communication of intelligent networks. In CS, fewer samples are required to reconstruct sparse or compressible signals, which breaks the restrict condition of a traditional Nyquist–Shannon sampling theorem. Here, we give an overview of recent CS studies, along the issues of sensing models, reconstruction algorithms, and their applications. First, we introduce several common sensing methods for CS, like sparse dictionary sensing, block-compressed sensing, and chaotic compressed sensing. We also present several state-of-the-art reconstruction algorithms of CS, including the convex optimization, greedy, and Bayesian algorithms. Lastly, we offer recommendation for broad CS applications, such as data compression, image processing, cryptography, and the reconstruction of complex networks. We discuss works related to CS technology and some CS essentials.

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