  Deep convolutional neural networks (CNNs) for image denoising are usually
trained on large datasets. These models achieve the current state of the art,
but they have difficulties generalizing when applied to data that deviate from
the training distribution. Recent work has shown that it is possible to train
denoisers on a single noisy image. These models adapt to the features of the
test image, but their performance is limited by the small amount of information
used to train them. Here we propose "GainTuning", in which CNN models
pre-trained on large datasets are adaptively and selectively adjusted for
individual test images. To avoid overfitting, GainTuning optimizes a single
multiplicative scaling parameter (the "Gain") of each channel in the
convolutional layers of the CNN. We show that GainTuning improves
state-of-the-art CNNs on standard image-denoising benchmarks, boosting their
denoising performance on nearly every image in a held-out test set. These
adaptive improvements are even more substantial for test images differing
systematically from the training data, either in noise level or image type. We
illustrate the potential of adaptive denoising in a scientific application, in
which a CNN is trained on synthetic data, and tested on real
transmission-electron-microscope images. In contrast to the existing
methodology, GainTuning is able to faithfully reconstruct the structure of
catalytic nanoparticles from these data at extremely low signal-to-noise
ratios.
