Image Compressed Sensing Recovery based on Multi-scale Group Sparse Representation

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.

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Image Fusion Method Based on Multi-scale Directional Fast Guided Filter and Convolutional Sparse Representation

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096511666180607101743 ◽

2019 ◽

Vol 12 (3) ◽

pp. 263-269

Author(s):

Liu Xian-Hong ◽

Chen Zhi-Bin

Keyword(s):

Image Fusion ◽

Sparse Representation ◽

Filter Bank ◽

Guided Filter ◽

Fusion Method ◽

Edge Preserving ◽

Directional Information ◽

Multi Scale ◽

Directional Filter ◽

Image Fusion Method

Background: A multi-scale multidirectional image fusion method is proposed, which introduces the Nonsubsampled Directional Filter Bank (NSDFB) into the multi-scale edge-preserving decomposition based on the fast guided filter. Methods: The proposed method has the advantages of preserving edges and extracting directional information simultaneously. In order to get better-fused sub-bands coefficients, a Convolutional Sparse Representation (CSR) based approximation sub-bands fusion rule is introduced and a Pulse Coupled Neural Network (PCNN) based detail sub-bands fusion strategy with New Sum of Modified Laplacian (NSML) to be the external input is also presented simultaneously. Results: Experimental results have demonstrated the superiority of the proposed method over conventional methods in terms of visual effects and objective evaluations. Conclusion: In this paper, combining fast guided filter and nonsubsampled directional filter bank, a multi-scale directional edge-preserving filter image fusion method is proposed. The proposed method has the features of edge-preserving and extracting directional information.

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Fusion of multi-scale DEMs from Chang’E-3 descent and Navcam images using compressed sensing method

Planetary Remote Sensing and Mapping ◽

10.1201/9780429505997-16 ◽

2018 ◽

pp. 241-250

Author(s):

M. Peng ◽

W. Wan ◽

Z. Liu ◽

K. Di

Keyword(s):

Compressed Sensing ◽

Multi Scale

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Study on Over-Complete Dictionaries for Sparse Representations of Signals

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.157-158.796 ◽

2012 ◽

Vol 157-158 ◽

pp. 796-799

Author(s):

Guang Chun Gao ◽

Kai Xiong ◽

Li Na Shang ◽

Sheng Ying Zhao ◽

Cui Zhang

Keyword(s):

Inverse Problems ◽

Compressed Sensing ◽

Sparse Representation ◽

Sparse Representations ◽

Experimental Results ◽

Decomposition Algorithm ◽

Linear Combinations ◽

Design Algorithm ◽

Sparse Decomposition

In recent years there has been a growing interest in the study of sparse representation of signals. The redundancy of over-complete dictionary can make it effectively capture the characteristics of the signals. Using an over-complete dictionary that contains prototype signal-atoms, signals are described as linear combinations of a few of these atoms. Applications that use sparse representation are many and include compression, regularization in inverse problems, Compressed Sensing (CS), and more. Recent activities in this field concentrate mainly on the study of sparse decomposition algorithm and dictionary design algorithm. In this paper, we discuss the advantages of sparse dictionaries, and present the implicit dictionaries for signal sparse presents. The overcomplete dictionaries which combined the different orthonormal transform bases can be used for the compressed sensing. Experimental results demonstrate the effectivity for sparse presents of signals.

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