  We describe a novel method for training high-quality image denoising models
based on unorganized collections of corrupted images. The training does not
need access to clean reference images, or explicit pairs of corrupted images,
and can thus be applied in situations where such data is unacceptably expensive
or impossible to acquire. We build on a recent technique that removes the need
for reference data by employing networks with a "blind spot" in the receptive
field, and significantly improve two key aspects: image quality and training
efficiency. Our result quality is on par with state-of-the-art neural network
denoisers in the case of i.i.d. additive Gaussian noise, and not far behind
with Poisson and impulse noise. We also successfully handle cases where
parameters of the noise model are variable and/or unknown in both training and
evaluation data.
