scholarly journals A DUAL-MESH MICROWAVE RECONSTRUCTION METHOD BASED ON COMPRESSIVE SAMPLING MATCHING PURSUIT ALGORITHM

2019 ◽  
Vol 166 ◽  
pp. 43-57
Author(s):  
Huiyuan Zhou ◽  
Ram M. Narayanan
Keyword(s):  
Matching Pursuit ◽  
Compressive Sampling ◽  
Reconstruction Method ◽  
Matching Pursuit Algorithm ◽  
Dual Mesh

scholarly journals Wavelet Compressive Sampling Signal Reconstruction Using Upside-Down Tree Structure

2011 ◽  
Vol 2011 ◽  
pp. 1-10
Author(s):  
Yijiu Zhao ◽  
Xiaoyan Zhuang ◽  
Zhijian Dai ◽  
Houjun Wang
Keyword(s):  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Signal Reconstruction ◽  
Compressive Sampling ◽  
Tree Structure ◽  
Orthogonal Matching Pursuit ◽  
Reconstruction Method ◽  
Wavelet Domain ◽  
Wavelet Coefficients ◽  
Signal To Noise

This paper suggests an upside-down tree-based orthogonal matching pursuit (UDT-OMP) compressive sampling signal reconstruction method in wavelet domain. An upside-down tree for the wavelet coefficients of signal is constructed, and an improved version of orthogonal matching pursuit is presented. The proposed algorithm reconstructs compressive sampling signal by exploiting the upside-down tree structure of the wavelet coefficients of signal besides its sparsity in wavelet basis. Compared with conventional greedy pursuit algorithms: orthogonal matching pursuit (OMP) and tree-based orthogonal matching pursuit (TOMP), signal-to-noise ratio (SNR) using UDT-OMP is significantly improved.

The Improvement of Compressive Sampling and Matching Pursuit Algorithm Based on Pre-estimation

2016 ◽  
Vol 23 (2) ◽  
pp. 129-134
Author(s):  
Guiling Sun ◽  
Yangyang Li ◽  
Haojie Yuan ◽  
Jingfei He ◽  
Tianyu Geng
Keyword(s):  
Matching Pursuit ◽  
Compressive Sampling ◽  
Matching Pursuit Algorithm

scholarly journals Hazardous Gas Emission Monitoring Based on High-Resolution Images

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Wenjian Chen ◽  
Yi Wang ◽  
Xuan Li ◽  
Wei Gao ◽  
Shiwei Ma ◽  
...  
Keyword(s):  
High Resolution ◽  
Matching Pursuit ◽  
Singular Value ◽  
Reconstruction Method ◽  
Crucial Importance ◽  
Gas Emission ◽  
Depth Data ◽  
High Resolution Images ◽  
Matching Pursuit Algorithm ◽  
Value Decomposition

Air pollution presents unprecedentedly severe challenges to humans today. Various measures have been taken to monitor pollution from gas emissions and the changing atmosphere, of which imaging is of crucial importance. By images of target scenes, intuitional judgments and in-depth data are achievable. However, due to the limitations of imaging devices, effective and efficient monitoring work is often hindered by low-resolution target images. To deal with this problem, a superresolution reconstruction method was proposed in this study for high-resolution monitoring images. It was based on the idea of sparse representation. Particularly, multiple dictionary pairs were trained according to the gradient features of samples, and one optimal pair of dictionaries was chosen to reconstruct by judging the weighting of the information in different directions. Furthermore, the K-means singular value decomposition algorithm was used to train the dictionaries and the orthogonal matching pursuit algorithm was employed to calculate the sparse coding coefficients. Finally, the experiment’s results demonstrated its advantages in both visual fidelity and numerical measures.

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