  Deep learning based image denoising methods have been recently popular due to
their improved performance. Traditionally, these methods are trained in a
supervised manner, requiring a set of noisy input and clean target image pairs.
More recently, self-supervised approaches have been proposed to learn denoising
from only noisy images. These methods assume that noise across pixels is
statistically independent, and the underlying image pixels show spatial
correlations across neighborhoods. These methods rely on a masking approach
that divides the image pixels into two disjoint sets, where one is used as
input to the network while the other is used to define the loss. However, these
previous self-supervised approaches rely on a purely data-driven regularization
neural network without explicitly taking the masking model into account. In
this work, building on these self-supervised approaches, we introduce
Noise2Inpaint (N2I), a training approach that recasts the denoising problem
into a regularized image inpainting framework. This allows us to use an
objective function, which can incorporate different statistical properties of
the noise as needed. We use algorithm unrolling to unroll an iterative
optimization for solving this objective function and train the unrolled network
end-to-end. The training paradigm follows the masking approach from previous
works, splitting the pixels into two disjoint sets. Importantly, one of these
is now used to impose data fidelity in the unrolled network, while the other
still defines the loss. We demonstrate that N2I performs successful denoising
on real-world datasets, while better preserving details compared to its purely
data-driven counterpart Noise2Self.
