Generalizable Denoising of Microscopy Images using Generative Adversarial Networks and Contrastive Learning
Félix Fuentes-Hurtado,
Jean-Baptiste Sibarita,
Virgile Viasnoff
Abstract:Microscopy images often suffer from high levels of noise, which can hinder further analysis and interpretation. Content-aware image restoration (CARE) methods have been proposed to address this issue, but they often require large amounts of training data and suffer from over-fitting. To overcome these challenges, we propose a novel framework for few-shot microscopy image denoising. Our approach combines a generative adversarial network (GAN) trained via contrastive learning (CL) with two structure preserving l… Show more
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