Cargando…
State-of-the-Art Approaches for Image Deconvolution Problems, including Modern Deep Learning Architectures
In modern digital microscopy, deconvolution methods are widely used to eliminate a number of image defects and increase resolution. In this review, we have divided these methods into classical, deep learning-based, and optimization-based methods. The review describes the major architectures of neura...
Autores principales: | Makarkin, Mikhail, Bratashov, Daniil |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8707587/ https://www.ncbi.nlm.nih.gov/pubmed/34945408 http://dx.doi.org/10.3390/mi12121558 |
Ejemplares similares
-
Detection of Rare Objects by Flow Cytometry: Imaging, Cell Sorting, and Deep Learning Approaches
por: Voronin, Denis V., et al.
Publicado: (2020) -
Effect of Size on Magnetic Polyelectrolyte Microcapsules Behavior: Biodistribution, Circulation Time, Interactions with Blood Cells and Immune System
por: Verkhovskii, Roman, et al.
Publicado: (2021) -
The Influence of Magnetic Composite Capsule Structure and Size on Their Trapping Efficiency in the Flow
por: Verkhovskii, Roman, et al.
Publicado: (2022) -
Correction: Verkhovskii et al. The Influence of Magnetic Composite Capsule Structure and Size on Their Trapping Efficiency in the Flow. Molecules 2022, 27, 6073
por: Verkhovskii, Roman, et al.
Publicado: (2023) -
Improved Approach for the Maximum Entropy Deconvolution Problem
por: Shlisel, Shay, et al.
Publicado: (2021)