Machine learning for cross-scale microscopy of viruses

A Petkidis, V Andriasyan, UF Greber - Cell Reports Methods, 2023 - cell.com
Despite advances in virological sciences and antiviral research, viruses continue to emerge,
circulate, and threaten public health. We still lack a comprehensive understanding of how …

Architecture and dynamics of a desmosome–endoplasmic reticulum complex

NK Bharathan, W Giang, CL Hoffman, JS Aaron… - Nature cell …, 2023 - nature.com
The endoplasmic reticulum (ER) forms a dynamic network that contacts other cellular
membranes to regulate stress responses, calcium signalling and lipid transfer. Here, using …

Live 4D-OCT denoising with self-supervised deep learning

J Nienhaus, P Matten, A Britten, J Scherer, E Höck… - Scientific Reports, 2023 - nature.com
By providing three-dimensional visualization of tissues and instruments at high resolution,
live volumetric optical coherence tomography (4D-OCT) has the potential to revolutionize …

Image denoising and the generative accumulation of photons

A Krull, H Basevi, B Salmon, A Zeug… - Proceedings of the …, 2024 - openaccess.thecvf.com
We present a fresh perspective on shot noise corrupted images and noise removal. By
viewing image formation as the sequential accumulation of photons on a detector grid, we …

Self-Calibrated Variance-Stabilizing Transformations for Real-World Image Denoising

S Herbreteau, M Unser - arXiv preprint arXiv:2407.17399, 2024 - arxiv.org
Supervised deep learning has become the method of choice for image denoising. It involves
the training of neural networks on large datasets composed of pairs of noisy and clean …

[PDF][PDF] Aprendizaje profundo para datos tomográficos

L Fernández Álvarez - 2023 - digibuo.uniovi.es
La tomografıa ha transformado de manera revolucionaria disciplinas como la medicina, la
ciencia de materiales y la biologıa al brindar una perspectiva no invasiva y no destructiva de …

Comparative Denoising Study Deep Learning & Collaborative Filter

S Kamoun - 2024 - diva-portal.org
This thesis addresses the challenge of denoising microscopy images captured under low-
light conditions with varying intensity levels. The study compares three deep learning …