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Removal or cancellation of noise has wide-spread applications for imaging andacoustics. In every-day-life applications, denoising may even includegenerative aspects, which are unfaithful to the ground truth. For scientificuse, however, denoising must reproduce the ground truth accurately. Here, weshow how data can be denoised via a deep convolutional neural network such thatweak signals appear with quantitative accuracy. In particular, we study X-raydiffraction on crystalline materials. We demonstrate that weak signals stemmingfrom charge ordering, insignificant in the noisy data, become visible andaccurate in the denoised data. This success is enabled by supervised trainingof a deep neural network with pairs of measured low- and high-noise data. Wedemonstrate that using artificial noise does not yield such quantitativelyaccurate results. Our approach thus illustrates a practical strategy for noisefiltering that can be applied to challenging acquisition problems.

Weak-signal extraction enabled by deep-neural-network denoising of diffraction data