2,427 research outputs found
Coordinate-based neural representations for computational adaptive optics in widefield microscopy
Widefield microscopy is widely used for non-invasive imaging of biological
structures at subcellular resolution. When applied to complex specimen, its
image quality is degraded by sample-induced optical aberration. Adaptive optics
can correct wavefront distortion and restore diffraction-limited resolution but
require wavefront sensing and corrective devices, increasing system complexity
and cost. Here, we describe a self-supervised machine learning algorithm,
CoCoA, that performs joint wavefront estimation and three-dimensional
structural information extraction from a single input 3D image stack without
the need for external training dataset. We implemented CoCoA for widefield
imaging of mouse brain tissues and validated its performance with
direct-wavefront-sensing-based adaptive optics. Importantly, we systematically
explored and quantitatively characterized the limiting factors of CoCoA's
performance. Using CoCoA, we demonstrated the first in vivo widefield mouse
brain imaging using machine-learning-based adaptive optics. Incorporating
coordinate-based neural representations and a forward physics model, the
self-supervised scheme of CoCoA should be applicable to microscopy modalities
in general.Comment: 33 pages, 5 figure
Fourier ptychography: current applications and future promises
Traditional imaging systems exhibit a well-known trade-off between the resolution and the field of view of their captured images. Typical cameras and microscopes can either “zoom in” and image at high-resolution, or they can “zoom out” to see a larger area at lower resolution, but can rarely achieve both effects simultaneously. In this review, we present details about a relatively new procedure termed Fourier ptychography (FP), which addresses the above trade-off to produce gigapixel-scale images without requiring any moving parts. To accomplish this, FP captures multiple low-resolution, large field-of-view images and computationally combines them in the Fourier domain into a high-resolution, large field-of-view result. Here, we present details about the various implementations of FP and highlight its demonstrated advantages to date, such as aberration recovery, phase imaging, and 3D tomographic reconstruction, to name a few. After providing some basics about FP, we list important details for successful experimental implementation, discuss its relationship with other computational imaging techniques, and point to the latest advances in the field while highlighting persisting challenges
Reports about 8 selected benchmark cases of model hierarchies : Deliverable number: D5.1 - Version 0.1
Based on the multitude of industrial applications, benchmarks for model hierarchies will be created that will form a basis for the interdisciplinary research and for the training programme. These will be equipped with publically available data and will be used for training in modelling, model testing, reduced order modelling, error estimation, efficiency optimization in algorithmic approaches, and testing of the generated MSO/MOR software. The present document includes the description about the selection of (at least) eight benchmark cases of model hierarchies.EC/H2020/765374/EU/Reduced Order Modelling, Simulation and Optimization of Coupled Systems/ROMSO
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