Synonyms
Noise reduction

nd-safir

Description

ND-SAFIR is a software for denoising n-dimentionnal images especially dedicated to microscopy image sequence analysis. It is able to deal with 2D, 3D, 2D+time, 3D+time images have one or more color channel. It is adapted to Gaussian and Poisson-Gaussian noise which are usually encountered in photonic imaging. Several papers describe the detail of the method used in ndsafir to recover noise free images (see references).

It is available either in Metamorph (commercial version), either as command line tool. Source are available on demand.

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CAREless

Description

Deep learning based image restoration methods have recently been made available to restore images from under-exposed imaging conditions, increase spatio-temporal resolution (CARE) or self-supervised image denoising (Noise2Void). These powerful methods outperform conventional state-of-the-art methods and leverage down-stream analyses significantly such as segmentation and quantification.

To bring these new tools to a broader platform in the image analysis community, we developed a simple Jupyter based graphical user interface for CARE and Noise2Void, which lowers the burden for non-programmers and biologists to access these powerful methods in their daily routine.  CARE-less supports temporal, multi-channel image and volumetric data and many file formats by using the bioformats library. The user is guided through the different computation steps via inline documentation. For standard use cases, the graphical user interface exposes the most relevant parameters such as patch size and number of training iterations, while expert users still have access to advanced parameters such as U-net depth and kernel sizes. In addition, CARE-less provides visual outputs for training convergence and restoration quality. Any project settings can be stored and reused from command line for processing on compute clusters. The generated output files preserve important meta-data such as pixel sizes, axial spacing and time intervals.

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BioImage Model Zoo

Description

This is a database of pretrained deep Learning models. 

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DeepImageJ

Description

DeepImageJ is a user-friendly plugin that enables the use of a variety of pre-trained deep learning models in ImageJ and Fiji. The plugin bridges the gap between deep learning and standard life-science applications. DeepImageJ runs image-to-image operations on a standard CPU-based computer and does not require any deep learning expertise.

Training developper constructs and upload trained model, and made them available to users.

Models are available in a repository here https://deepimagej.github.io/deepimagej/models.html

It is macro recordable. It is advised to luse DeepImageJ on a computer with GPU (CPU will likely be 20x slower)

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deepImageJ

clij - GPU-acceleration for ImageJ

Description

clij is an ImageJ/Fiji plugin allowing you to run GPU-accelerated code from within Fijis script editor (e.g. macro and jython). CLIJ is based on ClearCLImglib2 and SciJava. It contains components for image filtering, thresholding, spatial transforms, projections, binary image processing and basic signal measurements.

CSBDeep, a toolbox for Content-aware Image Restoration (CARE) in Knime

Description

Deep learning based restoration, with guidelines for training. See also the Fiji plugin.

CSBDeep, a toolbox for Content-aware Image Restoration (CARE) in Fiji

Description

Deep learning for fluorescence image restoration (denoising, deconvolution). Requires training on your data set but the procedure is described.

CARE

MorphoGraphX

Description

MorphoGraphX is a free Linux application for the visualization and analysis of 3D biological datasets. Developed by researchers, it is primarily used for the analysis and quantification of 3D live-imaged confocal data sets.

The main research interests adressed by MorphoGraphX are:

  • Shape extraction
  • Growth analysis
  • Signal quantification
  • Protein localization
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MorphoGraphX user interface

Super-Resolution Radial Fluctuations (SRRF)

Description

SRRF is a high-performance analytical approach for Live-cell Super-Resolution Microscopy, provided as a fast GPU-enabled ImageJ plugin. SRRF is capable of extracting high-fidelity super-resolution information from TIRF, widefield and confocals using conventional fluorophores such as GFP. SRRF is capable of live-cell imaging over timescales ranging from minutes to hours.

Comparison TIRF - SRRF

ImageJ Plugin for Non-Local-Means Filtering

Description

A more modern approach for denoising / smoothening before segmentation, works like Gaussian blurring but preserves edges and boundaries. Listed in Fiji update sites. ## Algorithm Algorithm description is in [this page](http://www.ipol.im/pub/art/2011/bcm_nlm/) 2612. ## Example usage Localization of Membrane bound protein in Arabidopsis meristem was analyzed using the non-local-mean filter for refining its position 2613. ## impression It's effect is somewhere between Gaussian blurring and anistropic diffusion.

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