论文标题
使用自适应的回忆横梁网络模拟图像Denoising
Analog Image Denoising with an Adaptive Memristive Crossbar Network
论文作者
论文摘要
图像传感器中的噪声导致了各种降级过滤器的发展。嘈杂的图像可能很难识别，并且通常需要几种类型的后处理补偿电路。本文提出了一个使用模拟内存神经计算网络实现的自适应denoising系统。提出的方法可以学习新的噪声，并可以与CMOS图像传感器单独集成或单独集成或单独集成到或单独地集成到或单独地。实现了三个脱氧网络配置，即（1）单层网络，（2）卷积网络和（3）融合网络。单层网络分别显示3.2us，每图像21nJ和0.3mm^2的处理时间，能耗和片上的区域，同时，卷积降级网络相应地显示了72ms，236UJ和0.48mm^2。在所有实施的网络中，可以观察到性能指标SSIM，MSE和PSNR的最大改善分别为3.61、21.7和7.7倍。
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.
