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Noise in image sensors led to the development of a whole range of denoisingfilters. A noisy image can become hard to recognize and often require severaltypes of post-processing compensation circuits. This paper proposes an adaptivedenoising system implemented using an analog in-memory neural computingnetwork. The proposed method can learn new noises and can be integrated into oralone with CMOS image sensors. Three denoising network configurations areimplemented namely, (1) single layer network, (2) convolution network, and (3)fusion network. The single layer network shows the processing time, energyconsumption, and on-chip area of 3.2us, 21nJ per image, and 0.3mm^2respectively, meanwhile, the convolution denoising network correspondinglyshows 72ms, 236uJ, and 0.48mm^2. Among all the implemented networks, it isobserved that performance metrics SSIM, MSE, and PSNR show a maximumimprovement of 3.61, 21.7, and 7.7 times respectively.

Analog Image Denoising with an Adaptive Memristive Crossbar Network