Fast Fourier Convolutions in Self-Supervised Neural Networks for Image Denoising
Date
2022
Authors
Journal Title
Journal ISSN
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Publisher
Tartu Ülikool
Abstract
Quality of digital images depends on a multitude of environmental and
equipment factors. In many cases our options for optimizing imaging conditions are
limited, and the acquired images turn out to be corrupted with noise. Recently, denoising
convolutional neural networks (CNN) have started to outperform classical denoising
algorithms. If approached naively, these networks require a lot of pairs of noisy and
clean images from the particular domain. In some fields (e.g. in biomedical imaging)
it is hard to collect such data in abundance. This limitation has accelerated a research
for self-supervised networks what can learn denoising just from noisy images alone.
However, such networks’ performance could be constrained by the the limited receptive
field of regular convolution.
To mitigate this problem, a new modification for CNNs was proposed: Fast Fourier
Convolution (FFC). Here, a global receptive field is achieved by using Fourier Transform
and convolving spectral representation. Global perception field can help CNNs to better
capture dependencies in image regions which are far apart. Given the ability of FFC
to enhance multiple state-of-the-art classification neural networks, we hypothesize that
denoising neural networks could also gain from its use.
In this work, we design multiple approaches for incorporating FFC into self-supervised
neural networks for image denoising. We evaluate these approaches on three diverse
benchmark datasets and compare them with both supervised and self-supervised methods.
We empirically show that FFC-enhanced denoising network achieves the state-of-theart
results on character dataset and shows comparable level of performance for both
grayscale and color natural images.
Description
Keywords
deep learning, neural networks