scholarly journals A Fuzzy Selection Compressive Sampling Matching Pursuit Algorithm for its Practical Application

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 144101-144124
Author(s):  
Hu Yunfeng ◽  
Zhao Liquan
Keyword(s):  
Matching Pursuit ◽  
Compressive Sampling ◽  
Practical Application ◽  
Matching Pursuit Algorithm

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

Solder joint imagery compressing and recovery based on compressive sensing

2014 ◽  
Vol 26 (3) ◽  
pp. 129-138 ◽  
Author(s):  
Huihuang Zhao ◽  
Yaonan Wang ◽  
Zhijun Qiao ◽  
Bin Fu
Keyword(s):  
Solder Joint ◽  
Compressive Sensing ◽  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Block Size ◽  
Compressive Sampling ◽  
Image Block ◽  
Content Type ◽  
Matching Pursuit Algorithm ◽  
Image Compressing

Purpose – The purpose of this paper is to develop an improved compressive sensing algorithm for solder joint imagery compressing and recovery. The improved algorithm can improve the performance in terms of peak signal to noise ratio (PSNR) of solder joint imagery recovery. Design/methodology/approach – Unlike the traditional method, at first, the image was transformed into a sparse signal by discrete cosine transform; then the solder joint image was divided into blocks, and each image block was transformed into a one-dimensional data vector. At last, a block compressive sampling matching pursuit was proposed, and the proposed algorithm with different block sizes was used in recovering the solder joint imagery. Findings – The experiments showed that the proposed algorithm could achieve the best results on PSNR when compared to other methods such as the orthogonal matching pursuit algorithm, greedy basis pursuit algorithm, subspace pursuit algorithm and compressive sampling matching pursuit algorithm. When the block size was 16 × 16, the proposed algorithm could obtain better results than when the block size was 8 × 8 and 4 × 4. Practical implications – The paper provides a methodology for solder joint imagery compressing and recovery, and the proposed algorithm can also be used in other image compressing and recovery applications. Originality/value – According to the compressed sensing (CS) theory, a sparse or compressible signal can be represented by a fewer number of bases than those required by the Nyquist theorem. The findings provide fundamental guidelines to improve performance in image compressing and recovery based on compressive sensing.

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