Traditionally, two things have made image deconvolution difficult: (1) the requirement of powerful computers and hours of computation time; and (2) the need to wade through the many algorithms—published and proprietary—for performing image deconvolution. Recent improvements in computer power such as quad processors have helped to overcome the computing power and time issues, while many references have been written to try and guide researchers through deconvolution algorithms.
Analyzes, processes and visualizes multi-dimensional microscopy images. BioImageXD puts open-source computer science tools for three-dimensional visualization and analysis into the hands of all researchers, through a user-friendly graphical interface tuned to the needs of biologists. BioImageXD has no restrictive licenses or undisclosed algorithms and enables publication of precise, reproducible and modifiable workflows. It allows simple construction of processing pipelines and should enable biologists to perform challenging analyses of complex processes.
Consists of a deconvolution method suitable for high-resolution in vivo biological imaging. ER-Decon utilizes a regularization functional constructed using an entropy-based formalism tailored to exploit general spatial characteristics of the fluorescence images, combined with second-order derivatives in the regularization functional. The software is able to work with 2D data. It can reveal unprecedented structural detail in data with extremely low levels of signal, and enables the study of dynamic cellular processes at unique exposure levels.
Provides a tool for 3D deconvolution of images from widefield, confocal, two-photon, light sheet and high content analysis (HCA) microscopes. Microvolution adjusts blurring and minimizes the effect of noise by using an inverse of the point spread function (PSF) to each point of the measured image. It is available as an ImageJ or µManager plug-in and is compatible with MetaMorph. A direct integration is possible through an API version.
Assists in managing deconvolution problems with a wavelet-domain regularization. Fast Multilevel Thresholded-Landweber Deconvolution Algorithm can reduce a quadratic data term to a regularization on the L1-norm of the wavelet coefficients of the solution. This software shows slow convergence for ill-conditioned operators arising for example in deconvolution. It makes wavelet-regularized deconvolution more accessible including with limited software.
Aims to quantify and analyze properties of individual cells in large fluorescent microscopy datasets. Cytokit includes a collection of open source tools that offers GPU-accelerated implementations of cycle registration and image deconvolution. It also provides an interactive user interface for characterizing the phenotypic features of cells alongside their spatial distribution.
You can access more results by creating a free plan account or unlimited content via a premium account.