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Related Experiment Video

Updated: May 20, 2026

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

Published on: April 27, 2021

Variational algorithms to remove stationary noise: applications to microscopy imaging.

Jérôme Fehrenbach¹, Pierre Weiss, Corinne Lorenzo

¹IMT-UMR5219 Laboratory, University of Toulouse, Toulouse 31042, France. jerome.fehrenbach@math.univtoulouse.fr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|July 4, 2012

Summary

This study introduces a new variational stationary noise remover algorithm to eliminate structured noise in images, outperforming traditional methods when white noise assumptions fail. It works as both an image restoration and a cartoon+texture decomposition technique.

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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

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Published on: February 12, 2014

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Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

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Published on: April 27, 2021

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Area of Science:

Image processing
Computational imaging
Scientific visualization

Background:

Traditional image denoising often assumes white noise, which is inadequate for structured patterns like stripes.
Existing methods fail when complex noise models are required, limiting applications in scientific imaging.

Purpose of the Study:

To present a novel framework and algorithm, the variational stationary noise remover (VSNR), for eliminating stationary noise from images.
To address limitations of current denoising techniques by handling non-Gaussian noise, such as structured patterns.
To demonstrate the algorithm's versatility across different imaging modalities.

Main Methods:

Developed a variational framework for image denoising.
Proposed an algorithm interpretable as both Bayesian restoration and cartoon+texture decomposition.
Applied the VSNR to diverse image datasets.

Main Results:

The VSNR effectively removes stationary noise, including structured patterns like stripes.
The algorithm provides a unified approach to image restoration and decomposition.
Successful denoising demonstrated on scanning electron microscope, FIB-nanotomography, and selective plane illumination microscopy images.

Conclusions:

The variational stationary noise remover is a robust method for handling complex noise in scientific images.
This approach offers improved image quality and data interpretation for various microscopy techniques.
The VSNR framework advances image processing for challenging scientific data.