  We propose a new grayscale image denoiser, dubbed as Neural Affine Image
Denoiser (Neural AIDE), which utilizes neural network in a novel way. Unlike
other neural network based image denoising methods, which typically apply
simple supervised learning to learn a mapping from a noisy patch to a clean
patch, we formulate to train a neural network to learn an \emph{affine} mapping
that gets applied to a noisy pixel, based on its context. Our formulation
enables both supervised training of the network from the labeled training
dataset and adaptive fine-tuning of the network parameters using the given
noisy image subject to denoising. The key tool for devising Neural AIDE is to
devise an estimated loss function of the MSE of the affine mapping, solely
based on the noisy data. As a result, our algorithm can outperform most of the
recent state-of-the-art methods in the standard benchmark datasets. Moreover,
our fine-tuning method can nicely overcome one of the drawbacks of the
patch-level supervised learning methods in image denoising; namely, a
supervised trained model with a mismatched noise variance can be mostly
corrected as long as we have the matched noise variance during the fine-tuning
step.
