Unsupervised Denoising for Signal-Dependent and Row-Correlated Imaging Noise

arXiv:2310.07887v2 Announce Type: replace-cross
Abstract: Accurate analysis of microscopy images is hindered by the presence of noise. This noise is usually signal-dependent and often additionally correlated along rows or columns of pixels. Current self- and unsupervised denoisers can address signal-dependent noise, but none can reliably remove noise that is also row- or column-correlated. Here, we present the first fully unsupervised deep learning-based denoiser capable of handling imaging noise that is row-correlated as well as signal-dependent. Our approach uses a Variational …

abstract analysis arxiv cs.cv current denoising eess.iv images imaging microscopy noise pixels signal type unsupervised