Analog Image Denoising with an Adaptive Memristive Crossbar Network

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 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 languageEnglish (US)
Title of host publicationIEEE International Symposium on Circuits and Systems, ISCAS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3453-3457
Number of pages5
ISBN (Electronic)9781665484855
DOIs
StatePublished - 2022
Event2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022 - Austin, United States
Duration: May 27 2022Jun 1 2022

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2022-May
ISSN (Print)0271-4310

Conference

Conference2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022
Country/TerritoryUnited States
CityAustin
Period05/27/2206/1/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Memristor
  • Near-Sensor Processing
  • Neural Networks
  • RRAM Denoising

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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