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Search · four archives
16 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…
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…
Zixiang Zhou, Yunshan Zhong, Zemin Zhang, Xianwen Ren
Computational deconvolution with single-cell RNA sequencing data as reference is pivotal to interpreting spatial transcriptomics data, but the current methods are limited to cell-type resolution. Here we present Redeconve, an algorithm to deconvolute spatial transcriptomics data at single-cell resolution, enabling…
Jahanzeb Saqib, Junil Kim
Spatial transcriptomics technologies have significantly enhanced the analysis of gene expression profiles by retaining the spatial information of intact tissue sections and enabling the possibility of a more profound comprehension of tissue structures and cellular relationships. Despite this, most platforms have…
Yiming Liu, Spozmai Panezai, Yutong Wang, Sjoerd Stallinga
Richardson-Lucy (RL) deconvolution optimizes the likelihood of the object estimate for an incoherent imaging system. It can offer an increase in contrast, but converges poorly, and shows enhancement of noise as the iteration progresses. We have discovered the underlying reason for this problematic convergence behaviour…
Tianyi Zhu, Yuduo Guo, Yi Zhang, Zhi Lu + 4 more
'Jiamin Wu' 'Qionghai Dai'] Title: Abstract. Significance Light-field microscopy has achieved success in various applications of life sciences that require high-speed volumetric imaging. However, existing light-field reconstruction algorithms degrade severely in low-light conditions, and the deconvolution process is…
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…
Fudong Xue, Lin Yuan, Wenting He, Zuo’ang Xiang + 6 more
Computational super-resolution (SR) methods enable nanoscale imaging from single-frame wide-field or spinning-disk confocal images without hardware modifications, yet face limitations: statistical restoration suffers from noise and artifacts, while deep learning methods typically lack generalizability. We introduce…
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…
Lulu Yan, Xiaoqiang Sun, Janet Kelso
The emergence of spatial transcriptomics (ST) has brought new opportunities for studying spatial heterogeneity of tissue architecture () and cellular interaction (). However, a major limitation of current ST technologies [e.g. Spatial Transcriptomics (), 10× Visium () and Slide-seq ()] is that the measured gene…
Sota Kawakami, Hiroyuki Kudo, Abel C. H. Chen
When scanning a document printed on both sides by using an electronic scanner, the printed material on the back (front) side may be transmitted to the front (back) side. This phenomenon is called show-through. The problem to remove the show-through from scanned images is called the show-through removal in the…
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'…
Elad Sunray, Gil Weinberg, Benzy Laufer, Ori Katz
Noninvasive optical imaging through complex scattering media presents a major challenge across multiple fields. State-of-the-art techniques, such as reflection matrix decomposition and neural networks, rely on multiple measurements with varying illumination within the sample decorrelation time, making their application…
Francesco Guzzi, Alessandra Gianoncelli, Fulvio Billè, Sergio Carrato + 2 more
Computational techniques allow breaking the limits of traditional imaging methods, such as time restrictions, resolution, and optics flaws. While simple computational methods can be enough for highly controlled microscope setups or just for previews, an increased level of complexity is instead required for advanced…