ArticleJournal of biophotonics2012
Total variation versus wavelet-based methods for image denoising in fluorescence lifetime imaging microscopy.
Article in Journal of biophotonics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- DetectingPlants (Basel, Switzerland) · 2025Article
- Tensor regularized total variation for denoising of third harmonic generation images of brain tumors.Journal of biophotonics · 2019Article
- A novel glomerular basement membrane segmentation using neutrsophic set and shearlet transform on microscopic images.Health information science and systems · 2017Article
- Efficient processing of fluorescence images using directional multiscale representations.Mathematical modelling of natural phenomena · 2014Article
- FRAP, FLIM, and FRET: Detection and analysis of cellular dynamics on a molecular scale using fluorescence microscopy.Molecular reproduction and developmentReview
Corrections and comments
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Authors and funding
2 authors.
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Abstract
We report the first application of wavelet-based denoising (noise removal) methods to time-domain box-car fluorescence lifetime imaging microscopy (FLIM) images and compare the results to novel total variation (TV) denoising methods. Methods were tested first on artificial images and then applied to low-light live-cell images. Relative to undenoised images, TV methods could improve lifetime precision up to 10-fold in artificial images, while preserving the overall accuracy of lifetime and amplitude values of a single-exponential decay model and improving local lifetime fitting in live-cell images. Wavelet-based methods were at least 4-fold faster than TV methods, but could introduce significant inaccuracies in recovered lifetime values. The denoising methods discussed can potentially enhance a variety of FLIM applications, including live-cell, in vivo animal, or endoscopic imaging studies, especially under challenging imaging conditions such as low-light or fast video-rate imaging.
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