  Image denoising is an essential tool in computational photography. Standard
denoising techniques, which use deep neural networks at their core, require
pairs of clean and noisy images for its training. If we do not possess the
clean samples, we can use blind-spot neural network architectures, which
estimate the pixel value based on the neighbouring pixels only. These networks
thus allow training on noisy images directly, as they by-design avoid trivial
solutions. Nowadays, the blind-spot is mostly achieved using shifted
convolutions or serialization. We propose a novel fully convolutional network
architecture that uses dilations to achieve the blind-spot property. Our
network improves the performance over the prior work and achieves
state-of-the-art results on established datasets.
