TY - GEN
T1 - Analog Image Denoising with an Adaptive Memristive Crossbar Network
AU - Krestinskaya, O.
AU - Salama, K.
AU - James, A. P.
N1 - Publisher Copyright:
© 2022 IEEE.
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
UR - http://www.scopus.com/inward/record.url?scp=85142467737&partnerID=8YFLogxK
U2 - 10.1109/ISCAS48785.2022.9937269
DO - 10.1109/ISCAS48785.2022.9937269
M3 - Conference contribution
AN - SCOPUS:85142467737
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
SP - 3453
EP - 3457
BT - IEEE International Symposium on Circuits and Systems, ISCAS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022
Y2 - 27 May 2022 through 1 June 2022
ER -