Analog Image Denoising with an Adaptive Memristive Crossbar Network

O. Krestinskaya; K. Salama; A. P. James

doi:10.1109/ISCAS48785.2022.9937269

Analog Image Denoising with an Adaptive Memristive Crossbar Network

O. Krestinskaya, K. Salama, A. P. James

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

5 Scopus citations

Abstract

Noise in image sensors led to the development of a whole range of denoising filters. A noisy image can become hard to recognize and often require several types of post-processing compensation circuits. This paper proposes an adaptive denoising system implemented using analog in-memory neural computing network. The proposed method can learn new noises and can be integrated into or alone with CMOS image sensors. Three denoising network configurations are implemented, namely, (1) single layer network, (2) convolution network, and (3) fusion network. The single layer network shows the processing time, energy consumption and on-chip area of 3.2 mus, 21n J per image and 0.3mm2 respectively, meanwhile, convolution denoising network correspondingly shows 72m s, 236 muJ and 0.48mm2. Among all the implemented networks, it is observed that performance metrics SSIM, MSE and PSNR show a maximum improvement of 3.61, 21.7 and 7.7 times respectively.

Original language	English (US)
Title of host publication	IEEE International Symposium on Circuits and Systems, ISCAS 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3453-3457
Number of pages	5
ISBN (Electronic)	9781665484855
DOIs	https://doi.org/10.1109/ISCAS48785.2022.9937269
State	Published - 2022
Event	2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022 - Austin, United States Duration: May 27 2022 → Jun 1 2022

Publication series

Name	Proceedings - IEEE International Symposium on Circuits and Systems
Volume	2022-May
ISSN (Print)	0271-4310

Conference

Conference	2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022
Country/Territory	United States
City	Austin
Period	05/27/22 → 06/1/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Memristor
Near-Sensor Processing
Neural Networks
RRAM Denoising

ASJC Scopus subject areas

Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ISCAS48785.2022.9937269

Cite this

Krestinskaya, O., Salama, K., & James, A. P. (2022). Analog Image Denoising with an Adaptive Memristive Crossbar Network. In IEEE International Symposium on Circuits and Systems, ISCAS 2022 (pp. 3453-3457). (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2022-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS48785.2022.9937269

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title = "Analog Image Denoising with an Adaptive Memristive Crossbar Network",

abstract = "Noise in image sensors led to the development of a whole range of denoising filters. A noisy image can become hard to recognize and often require several types of post-processing compensation circuits. This paper proposes an adaptive denoising system implemented using analog in-memory neural computing network. The proposed method can learn new noises and can be integrated into or alone with CMOS image sensors. Three denoising network configurations are implemented, namely, (1) single layer network, (2) convolution network, and (3) fusion network. The single layer network shows the processing time, energy consumption and on-chip area of 3.2 mus, 21n J per image and 0.3mm2 respectively, meanwhile, convolution denoising network correspondingly shows 72m s, 236 muJ and 0.48mm2. Among all the implemented networks, it is observed that performance metrics SSIM, MSE and PSNR show a maximum improvement of 3.61, 21.7 and 7.7 times respectively.",

keywords = "Memristor, Near-Sensor Processing, Neural Networks, RRAM Denoising",

author = "O. Krestinskaya and K. Salama and James, {A. P.}",

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year = "2022",

doi = "10.1109/ISCAS48785.2022.9937269",

language = "English (US)",

series = "Proceedings - IEEE International Symposium on Circuits and Systems",

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Krestinskaya, O , Salama, K & James, AP 2022, Analog Image Denoising with an Adaptive Memristive Crossbar Network. in IEEE International Symposium on Circuits and Systems, ISCAS 2022. Proceedings - IEEE International Symposium on Circuits and Systems, vol. 2022-May, Institute of Electrical and Electronics Engineers Inc., pp. 3453-3457, 2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022, Austin, United States, 05/27/22. https://doi.org/10.1109/ISCAS48785.2022.9937269

Analog Image Denoising with an Adaptive Memristive Crossbar Network. / Krestinskaya, O.; Salama, K.; James, A. P.
IEEE International Symposium on Circuits and Systems, ISCAS 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 3453-3457 (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2022-May).

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

TY - GEN

T1 - Analog Image Denoising with an Adaptive Memristive Crossbar Network

AU - Krestinskaya, O.

AU - Salama, K.

AU - James, A. P.

PY - 2022

Y1 - 2022

N2 - Noise in image sensors led to the development of a whole range of denoising filters. A noisy image can become hard to recognize and often require several types of post-processing compensation circuits. This paper proposes an adaptive denoising system implemented using analog in-memory neural computing network. The proposed method can learn new noises and can be integrated into or alone with CMOS image sensors. Three denoising network configurations are implemented, namely, (1) single layer network, (2) convolution network, and (3) fusion network. The single layer network shows the processing time, energy consumption and on-chip area of 3.2 mus, 21n J per image and 0.3mm2 respectively, meanwhile, convolution denoising network correspondingly shows 72m s, 236 muJ and 0.48mm2. Among all the implemented networks, it is observed that performance metrics SSIM, MSE and PSNR show a maximum improvement of 3.61, 21.7 and 7.7 times respectively.

AB - Noise in image sensors led to the development of a whole range of denoising filters. A noisy image can become hard to recognize and often require several types of post-processing compensation circuits. This paper proposes an adaptive denoising system implemented using analog in-memory neural computing network. The proposed method can learn new noises and can be integrated into or alone with CMOS image sensors. Three denoising network configurations are implemented, namely, (1) single layer network, (2) convolution network, and (3) fusion network. The single layer network shows the processing time, energy consumption and on-chip area of 3.2 mus, 21n J per image and 0.3mm2 respectively, meanwhile, convolution denoising network correspondingly shows 72m s, 236 muJ and 0.48mm2. Among all the implemented networks, it is observed that performance metrics SSIM, MSE and PSNR show a maximum improvement of 3.61, 21.7 and 7.7 times respectively.

KW - Memristor

KW - Near-Sensor Processing

KW - Neural Networks

KW - RRAM Denoising

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ER -

Analog Image Denoising with an Adaptive Memristive Crossbar Network

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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