Analog Image Denoising with an Adaptive Memristive Crossbar Network

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$\mu$s, 21n J per image and 0.3mm
²
respectively, meanwhile, convolution denoising network correspondingly shows 72m s, 236$\mu$J and 0.48mm
²
. 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.