  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 an 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 3.2us, 21nJ per image, and 0.3mm^2
respectively, meanwhile, the convolution denoising network correspondingly
shows 72ms, 236uJ, 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.
