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Search · four archives
15 papers · ranked by Valyu relevance
Mikhail Makarkin, Daniil Bratashov, Aiqun Liu
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 neural networks, such as…
Maria K Jaakkola, Laura L Elo
Computational deconvolution is a time and cost-efficient approach to obtain cell type-specific information from bulk gene expression of heterogeneous tissues like blood. Deconvolution can aim to either estimate cell type proportions or abundances in samples, or estimate how strongly each present cell type expresses…
Maísa R Ferro dos Santos, Edoardo Giuili, Andries De Koker, Celine Everaert + 1 more
'Celine Everaert' 'Katleen De Preter'] Title: Abstract In this review, we provide a comprehensive overview of the different computational tools that have been published for the deconvolution of bulk DNA methylation (DNAm) data. Here, deconvolution refers to the estimation of cell-type proportions that constitute a…
Luís M. A. Perdigão, Casper Berger, Neville B.-Y. Yee, Michele C. Darrow + 1 more
The experimental limitations with optics observed in many microscopy and astronomy instruments result in detrimental effects for the imaging of objects. This can be generally described mathematically as a convolution of the real object image with the point spread function that characterizes the optical system. The…
Hayato Ikoma, Michael Broxton, Takamasa Kudo, Gordon Wetzstein
Deconvolution is widely used to improve the contrast and clarity of a 3D focal stack collected using a fluorescence microscope. But despite being extensively studied, deconvolution algorithms can introduce reconstruction artifacts when their underlying noise models or priors are violated, such as when imaging…
R. Zanella, G. Zanghirati, R. Cavicchioli, L. Zanni + 3 more
'M. Bertero' 'G. Vicidomini'] Although deconvolution can improve the quality of any type of microscope, the high computational time required has so far limited its massive spreading. Here we demonstrate the ability of the scaled-gradient-projection (SGP) method to provide accelerated versions of the most used…
Stefania Perri, Cristian Sestito, Fanny Spagnolo, Pasquale Corsonello
'Pasquale Corsonello'] Today, convolutional and deconvolutional neural network models are exceptionally popular thanks to the impressive accuracies they have been proven in several computer-vision applications. To speed up the overall tasks of these neural networks, purpose-designed accelerators are highly desirable.…
Amit Kohli, Anastasios N. Angelopoulos, David McAllister, Esther Whang + 6 more
'Esther Whang' 'Sixian You' 'Kyrollos Yanny' 'Federico M. Gasparoli' 'Bo-Jui Chang' 'Reto Fiolka' 'Laura Waller'] The most ubiquitous form of aberration correction for microscopy is deconvolution; however, deconvolution relies on the assumption that the system’s point spread function is the same across the entire field…
Wenfeng Tian, Riwang Chen, Liangyi Chen
Super-Resolution: An Odyssey in Harnessing Priors to Enhance Optical Microscopy Resolution Authors: Wenfeng Tian, Riwang Chen, Liangyi Chen A simple iterative algorithm for the extrapolation problem, based on the finite spatial extent prior, is the Gerchberg-Papoulis (GP) algorithm, proposed in the 1970s.47,48 We defer…
Yufei Gao, Weiwei Yin, Wei Hu, Wei Chen
Single-cell sequencing is revolutionizing biological research by enabling unprecedented cellular resolution, yet traditional multi-sample experiments are often constrained by high costs and batch effects. Sample multiplexing offers a critical solution by uniquely tagging individual cells from diverse samples for pooled…
M Laasmaa, M Vendelin, P Peterson
Although confocal microscopes have considerably smaller contribution of out-of-focus light than widefield microscopes, the confocal images can still be enhanced mathematically if the optical and data acquisition effects are accounted for. For that, several deconvolution algorithms have been proposed. As a practical…
Boyoung Kim, Doug Brooks, Alexandra Sorvina, Shane Hickey
To investigate the cellular structure, biomedical researchers often obtain three-dimensional images by combining two-dimensional images taken along the z axis. However, these images are blurry in all directions due to diffraction limitations. This blur becomes more severe when focusing further inside the specimen as…
Alexander Wong, Xiao Yu Wang, Maud Gorbet
Fluorescence microscopy is widely used for the study of biological specimens. Deconvolution can significantly improve the resolution and contrast of images produced using fluorescence microscopy; in particular, Bayesian-based methods have become very popular in deconvolution fluorescence microscopy. An ongoing…
Boyoung Kim, Takeshi Naemura
This paper proposes a new deconvolution method for 3D fluorescence wide-field microscopy. Most previous methods are insufficient in terms of restoring a 3D cell structure, since a point spread function (PSF) is simply assumed as depth-invariant, whereas a PSF of microscopy changes significantly along the optical axis.…
Joseph Rosen, Simon Alford, Blake Allan, Vijayakumar Anand + 79 more
'Shlomi Arnon' 'Francis Gracy Arockiaraj' 'Jonathan Art' 'Bijie Bai' 'Ganesh M. Balasubramaniam' 'Tobias Birnbaum' 'Nandan S. Bisht' 'David Blinder' 'Liangcai Cao' 'Qian Chen' 'Ziyang Chen' 'Vishesh Dubey' 'Karen Egiazarian' 'Mert Ercan' 'Andrew Forbes' 'G. Gopakumar' 'Yunhui Gao' 'Sylvain Gigan' 'Paweł Gocłowski'…