A high-speed image super-resolution algorithm based on sparse representation for MEMS defect detection

Xiuyuan Li; Yulong Zhao; Tengjiang Hu; Qi Zhang; Yingxue Li

doi:10.1109/NEMS.2016.7758239

A high-speed image super-resolution algorithm based on sparse representation for MEMS defect detection

Xiuyuan Li
, Yulong Zhao
, Tengjiang Hu
, Qi Zhang
, Yingxue Li

School of Mechanical Engineering

Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

A novel high- speed image super-resolution algorithm based on sparse representation for MEMS defect detection is proposed in this paper. Traditional super-resolution algorithms adopt a single dictionary to represent images, which cannot differentiate varieties of image blocks and leads to slow processing speed. Aiming at overcoming this shortage of traditional super-resolution algorithms, image blocks are divided into different categories by local features and each of these categories possesses the corresponding high and low resolution dictionary pairs. Experimental results of different MEMS defects show that the improved algorithm can obtain images of little lower quality with much less processing time, indicating that the proposed algorithm is more suitable for MEMS defect detection.

Original language	English
Title of host publication	2016 IEEE 11th Annual International Conference on Nano/Micro Engineered and Molecular Systems, NEMS 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	229-232
Number of pages	4
ISBN (Electronic)	9781509019472
DOIs	https://doi.org/10.1109/NEMS.2016.7758239
State	Published - 28 Nov 2016
Event	11th IEEE Annual International Conference on Nano/Micro Engineered and Molecular Systems, NEMS 2016 - Sendai, Japan Duration: 17 Apr 2016 → 20 Apr 2016

Publication series

Name	2016 IEEE 11th Annual International Conference on Nano/Micro Engineered and Molecular Systems, NEMS 2016

Conference

Conference	11th IEEE Annual International Conference on Nano/Micro Engineered and Molecular Systems, NEMS 2016
Country/Territory	Japan
City	Sendai
Period	17/04/16 → 20/04/16

Keywords

MEMS defect detection
fast
image super-resolution
sparse representation

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10.1109/NEMS.2016.7758239

Cite this

Li, X., Zhao, Y., Hu, T., Zhang, Q., & Li, Y. (2016). A high-speed image super-resolution algorithm based on sparse representation for MEMS defect detection. In 2016 IEEE 11th Annual International Conference on Nano/Micro Engineered and Molecular Systems, NEMS 2016 (pp. 229-232). Article 7758239 (2016 IEEE 11th Annual International Conference on Nano/Micro Engineered and Molecular Systems, NEMS 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NEMS.2016.7758239

Li, Xiuyuan ; Zhao, Yulong ; Hu, Tengjiang et al. / A high-speed image super-resolution algorithm based on sparse representation for MEMS defect detection. 2016 IEEE 11th Annual International Conference on Nano/Micro Engineered and Molecular Systems, NEMS 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 229-232 (2016 IEEE 11th Annual International Conference on Nano/Micro Engineered and Molecular Systems, NEMS 2016).

@inproceedings{9461e3f2797a4cee8b33f3fd7bb77b88,

title = "A high-speed image super-resolution algorithm based on sparse representation for MEMS defect detection",

abstract = "A novel high- speed image super-resolution algorithm based on sparse representation for MEMS defect detection is proposed in this paper. Traditional super-resolution algorithms adopt a single dictionary to represent images, which cannot differentiate varieties of image blocks and leads to slow processing speed. Aiming at overcoming this shortage of traditional super-resolution algorithms, image blocks are divided into different categories by local features and each of these categories possesses the corresponding high and low resolution dictionary pairs. Experimental results of different MEMS defects show that the improved algorithm can obtain images of little lower quality with much less processing time, indicating that the proposed algorithm is more suitable for MEMS defect detection.",

keywords = "MEMS defect detection, fast, image super-resolution, sparse representation",

author = "Xiuyuan Li and Yulong Zhao and Tengjiang Hu and Qi Zhang and Yingxue Li",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 11th IEEE Annual International Conference on Nano/Micro Engineered and Molecular Systems, NEMS 2016 ; Conference date: 17-04-2016 Through 20-04-2016",

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series = "2016 IEEE 11th Annual International Conference on Nano/Micro Engineered and Molecular Systems, NEMS 2016",

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Li, X, Zhao, Y, Hu, T, Zhang, Q & Li, Y 2016, A high-speed image super-resolution algorithm based on sparse representation for MEMS defect detection. in 2016 IEEE 11th Annual International Conference on Nano/Micro Engineered and Molecular Systems, NEMS 2016., 7758239, 2016 IEEE 11th Annual International Conference on Nano/Micro Engineered and Molecular Systems, NEMS 2016, Institute of Electrical and Electronics Engineers Inc., pp. 229-232, 11th IEEE Annual International Conference on Nano/Micro Engineered and Molecular Systems, NEMS 2016, Sendai, Japan, 17/04/16. https://doi.org/10.1109/NEMS.2016.7758239

A high-speed image super-resolution algorithm based on sparse representation for MEMS defect detection. / Li, Xiuyuan; Zhao, Yulong; Hu, Tengjiang et al.
2016 IEEE 11th Annual International Conference on Nano/Micro Engineered and Molecular Systems, NEMS 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 229-232 7758239 (2016 IEEE 11th Annual International Conference on Nano/Micro Engineered and Molecular Systems, NEMS 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Li, Yingxue

PY - 2016/11/28

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N2 - A novel high- speed image super-resolution algorithm based on sparse representation for MEMS defect detection is proposed in this paper. Traditional super-resolution algorithms adopt a single dictionary to represent images, which cannot differentiate varieties of image blocks and leads to slow processing speed. Aiming at overcoming this shortage of traditional super-resolution algorithms, image blocks are divided into different categories by local features and each of these categories possesses the corresponding high and low resolution dictionary pairs. Experimental results of different MEMS defects show that the improved algorithm can obtain images of little lower quality with much less processing time, indicating that the proposed algorithm is more suitable for MEMS defect detection.

AB - A novel high- speed image super-resolution algorithm based on sparse representation for MEMS defect detection is proposed in this paper. Traditional super-resolution algorithms adopt a single dictionary to represent images, which cannot differentiate varieties of image blocks and leads to slow processing speed. Aiming at overcoming this shortage of traditional super-resolution algorithms, image blocks are divided into different categories by local features and each of these categories possesses the corresponding high and low resolution dictionary pairs. Experimental results of different MEMS defects show that the improved algorithm can obtain images of little lower quality with much less processing time, indicating that the proposed algorithm is more suitable for MEMS defect detection.

KW - MEMS defect detection

KW - fast

KW - image super-resolution

KW - sparse representation

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Li X, Zhao Y, Hu T, Zhang Q, Li Y. A high-speed image super-resolution algorithm based on sparse representation for MEMS defect detection. In 2016 IEEE 11th Annual International Conference on Nano/Micro Engineered and Molecular Systems, NEMS 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 229-232. 7758239. (2016 IEEE 11th Annual International Conference on Nano/Micro Engineered and Molecular Systems, NEMS 2016). doi: 10.1109/NEMS.2016.7758239

A high-speed image super-resolution algorithm based on sparse representation for MEMS defect detection

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