Memristor bridge-based low pass filter for image processing

doi:10.21629/JSEE.2019.03.02

Journal of Systems Engineering and Electronics ›› 2019, Vol. 30 ›› Issue (3): 448-455.doi: 10.21629/JSEE.2019.03.02

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Memristor bridge-based low pass filter for image processing

Yongbin YU^1,*(), Nijing YANG¹(), Chenyu YANG¹(), Tashi NYIMA²()

¹ School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
² School of Information Science and Technology, Tibet University, Lhasa 850012, China

Received:2018-07-05 Online:2019-06-01 Published:2019-07-04
Contact: Yongbin YU E-mail:ybyu@uestc.edu.cn;yang_nijing@163.com;chenyu_yang_divine@163.com;niqiongda@163.com
About author:YU Yongbin was born in 1975. He received his Ph.D. degree from the University of Electronic Science and Technology of China (UESTC), Chengdu, in 2008. He is currently an associate professor in the School of Information and Software Engineering, UESTC. His research interests include big data and memristor. E-mail:ybyu@uestc.edu.cn|YANG Nijing was born in 1994. She received her B.S. degree from the North University of China, Taiyuan, in 2016. She is currently pursuing her Ph.D. degree with the School of information and software engineering, UESTC. Her research interests include memristive system, memristive filter and image processing. E-mail:yang_nijing@163.com|YANG Chenyu was born in 1994. He received his B.S. degree from the UESTC, Chengdu, in 2016. He is currently pursuing his M.S. degree with the School of Information and Software Engineering, UESTC. His research interests include memristive system, memristive filter and image processing. E-mail:chenyu_yang_divine@163.com|NYIMA Tashi was born in 1964. He received his Ph.D. degree from the Sichuan University, Chengdu, in 2009. He is currently a professor in the Tibet University. His research interests include computer network and information system. E-mail:niqiongda@163.com
Supported by:
the National Natural Science Foundation of China(61550110248);This work was supported by the National Natural Science Foundation of China (61550110248)

Abstract

Abstract:

This paper highlights the memristor bridge-based lowpass filter (LPF) and improved image processing algorithms along with a novel adaptive Gaussian filter for denoising image and a new Gaussian pyramid for scale invariant feature transform (SIFT). First, a novel kind of LPF based on the memristor bridge is designed, whose cut-off frequency and other traits are demonstrated to change with different time and memristance. In light of the changeable parameter of the memristor bridge-based LPF, a new adaptive Gaussian filter and an improved SIFT algorithm are presented. Finally, experiment results show that the peak signalto-noise ratio (PSNR) of our denoising is bettered more than 2.77 dB compared to the corresponding of the traditional Gaussian filter, and our improved SIFT performances including the number of matched feature points and the percent of correct matches are higher than the traditional SIFT, which verifies feasibility and effectiveness of our algorithm.

Key words: memristor bridge, low-pass filter (LPF), adaptive Gaussian filter, image denoising, Gaussian pyramid

Yongbin YU, Nijing YANG, Chenyu YANG, Tashi NYIMA. Memristor bridge-based low pass filter for image processing[J]. Journal of Systems Engineering and Electronics, 2019, 30(3): 448-455.

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References 31

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MSE	Images
MSE	No.1	No.2	No.3	No.4	No.5
Noisy	53.392 3	53.859 5	53.885 8	54.286 2	52.916 4
Original filter [27]	6.478 1	10.241 0	10.023 6	13.069 2	8.386 6
Adaptive filter	2.490 9	5.400 1	3.076 8	3.819 7	3.055 5
MSE reduced	3.987 2	4.840 9	6.946 8	9.245 0	5.331 1

PSNR	Images
PSNR	No.1	No.2	No.3	No.4	No.5
Noisy	30.856 0	30.818 2	30.816 1	30.783 9	30.894 9
Original filter [27]	40.016 3	38.027 4	38.120 6	36.968 3	38.895 0
Adaptive filter	44.167 2	40.806 8	43.249 8	42.310 5	43.279 9
PSNR improved	4.150 9	2.779 4	5.129 2	5.342 2	4.384 9

Image	Number of feature points	Matched	Correct matches	Percent of correct matches/%
(a)	5 524	1 566	1 411	90
(b)	4 327
(c)	4 111	1 239	828	67
(d)	2 845
(e)	4 584	1 041	935	90
(f)	4 186
(g)	9 763	772	754	98
(h)	6 854

Image	Number of feature points	Matched	Correct matches	Percent of correct matches/%
(a)	12 787	4 164	4 085	97
(b)	10 640
(c)	9 781	1 472	1 062	72
(d)	6 161
(e)	6 305	1 480	1 410	95
(f)	6 176
(g)	29 938	975	966	99
(h)	14 093

Memristor bridge-based low pass filter for image processing

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