  In the last several years deep learning based approaches have come to
dominate many areas of computer vision, and image denoising is no exception.
Neural networks can learn by example to map noisy images to clean images.
However, access to noisy/clean or even noisy/noisy image pairs isn't always
readily available in the desired domain. Recent approaches have allowed for the
denoising of single noisy images without access to any training data aside from
that very image. But since they require both training and inference to be
carried out on each individual input image, these methods require significant
computation time. As such, they are difficult to integrate into automated
microscopy pipelines where denoising large datasets is essential but needs to
be carried out in a timely manner. Here we present Noise2Fast, a fast single
image blind denoiser. Our method is tailored for speed by training on a
four-image dataset produced using a unique form of downsampling we refer to as
"checkerboard downsampling". Noise2Fast is faster than all tested approaches
and is more accurate than all except Self2Self, which takes well over 100 times
longer to denoise an image. This allows for a combination of speed and
flexibility that was not previously attainable using any other method.
