Suppression of Correlated Noise with Similarity-based Unsupervised Deep Learning. (arXiv:2011.03384v6 [cs.LG] UPDATED)

Image denoising is a prerequisite for downstream tasks in many fields.
Low-dose and photon-counting computed tomography (CT) denoising can optimize
diagnostic performance at minimized radiation dose. Supervised deep denoising
methods are popular but require paired clean or noisy samples that are often
unavailable in practice. Limited by the independent noise assumption, current
unsupervised denoising methods cannot process correlated noises as in CT
images. Here we propose the first-of-its-kind similarity-based unsupervised
deep denoising approach, referred to as Noise2Sim, that works in …

arxiv deep learning learning noise unsupervised