SwinIA: Self-Supervised Blind-Spot Image Denoising with Zero
  Convolutions
Mikhail Papkov Pavel Chizhov
The essence of self-supervised image denoising is to restore the signal from
the noisy image alone. State-of-the-art solutions for this task rely on the
idea of masking pixels and training a fully-convolutional neural network to
impute them. This most often requires multiple forward passes, information
about the noise model, and intricate regularization functions. In this paper,
we propose a Swin Transformer-based Image Autoencoder (SwinIA), the first
convolution-free architecture for self-supervised denoising. It can be trained
end-to-end with a simple mean squared error loss without masking and does not
require any prior knowledge about clean data or noise distribution. Despite its
simplicity, SwinIA establishes state-of-the-art on several common benchmarks.