13 papers · ranked by Valyu relevance
Sami Koho, Giorgio Tortarolo, Marco Castello, Takahiro Deguchi + 2 more
'Alberto Diaspro' 'Giuseppe Vicidomini'] Fourier ring correlation (FRC) has recently gained popularity among fluorescence microscopists as a straightforward and objective method to measure the effective image resolution. While the knowledge of the numeric resolution value is helpful in e.g., interpreting imaging…
Klaus Becker, Saiedeh Saghafi, Marko Pende, Inna Sabdyusheva-Litschauer + 4 more
'Inna Sabdyusheva-Litschauer' 'Christian M. Hahn' 'Massih Foroughipour' 'Nina Jährling' 'Hans-Ulrich Dodt'] We developed a deconvolution software for light sheet microscopy that uses a theoretical point spread function, which we derived from a model of image formation in a light sheet microscope. We show that this…
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…
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…
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…
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…
Octavi Fors, Jorge Núñez, Xavier Otazu, Albert Prades + 1 more
'Robert D. Cardinal'] In this paper we show how the techniques of image deconvolution can increase the ability of image sensors as, for example, CCD imagers, to detect faint stars or faint orbital objects (small satellites and space debris). In the case of faint stars, we show that this benefit is equivalent to double…
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…
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…
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…
Yiming Liu, Sjoerd Stallinga
The gradient-consensus Richardson-Lucy (GC-RL) deconvolution algorithm is a novel approach to contrast restoration in high-resolution microscopy imaging without excessive noise amplification. We evaluate this method in this work, focusing on the impact of the noise level of the input image, of imperfections and…
Philip Wijesinghe, Stella Corsetti, Darren J. X. Chow, Shuzo Sakata + 2 more
'Kylie R. Dunning' 'Kishan Dholakia'] Deconvolution is a challenging inverse problem, particularly in techniques that employ complex engineered point-spread functions, such as microscopy with propagation-invariant beams. Here, we present a deep-learning method for deconvolution that, in lieu of end-to-end training with…
Alexander Sachuk, Ekaterina Volkova, Anastasiya Rakovskaya, Vyacheslav Chukanov + 2 more
Fluorescence microscopy performance can be significantly enhanced with image post-processing algorithms, particularly deconvolution techniques. These methods aim to revert optical aberrations by deconvolving the image with the point spread function (PSF) of the microscope. However, analytical deconvolution algorithms…