  With the advent of advances in self-supervised learning, paired clean-noisy
data are no longer required in deep learning-based image denoising. However,
existing blind denoising methods still require the assumption with regard to
noise characteristics, such as zero-mean noise distribution and pixel-wise
noise-signal independence; this hinders wide adaptation of the method in the
medical domain. On the other hand, unpaired learning can overcome limitations
related to the assumption on noise characteristics, which makes it more
feasible for collecting the training data in real-world scenarios. In this
paper, we propose a novel image denoising scheme, Interdependent
Self-Cooperative Learning (ISCL), that leverages unpaired learning by combining
cyclic adversarial learning with self-supervised residual learning. Unlike the
existing unpaired image denoising methods relying on matching data
distributions in different domains, the two architectures in ISCL, designed for
different tasks, complement each other and boost the learning process. To
assess the performance of the proposed method, we conducted extensive
experiments in various biomedical image degradation scenarios, such as noise
caused by physical characteristics of electron microscopy (EM) devices (film
and charging noise), and structural noise found in low-dose computer tomography
(CT). We demonstrate that the image quality of our method is superior to
conventional and current state-of-the-art deep learning-based image denoising
methods, including supervised learning.
