Unsupervised Noise Reduction for Nanochannel Measurement Using Noise2Noise Deep Learning

Noise reduction is an important issue in measurement. A difficulty to train a noise reduction model using machine learning is that clean signal on measurement object needed for supervised training is hardly available in most advanced measurement problems. Recently, an unsupervised technique for training a noise reduction model called Noise2Noise has been proposed, and a deep learning model named U-net trained by this technique has demonstrated promising performance in some measurement problems. In this study, we applied this technique to highly noisy signals of electric current waveforms obtained by measuring nanoparticle passages in a multistage narrowing nanochannel. We found that a convolutional AutoEncoder (CAE) was more suitable than the U-net for the noise reduction using the Noise2Noise technique in the nanochannel measurement problem.