title: Denoising in fluorescence microscopy using compressed sensing with multiple reconstructions and non-local merging.
abstract: The cross-dependency of noise level and photobleaching in microscopy was discussed in a previous work and an efficient compressed sensing (CS) method was proposed to simultaneously reduce the noise level and the photobleaching. Here we present an improved CS denoising framework for fluorescence microscopy images, exploiting Non-Local means filtering to merge multiple reconstructions. This framework enables high-quality reconstruction of low exposed microscopy images based on random Fourier sampling schemes and multiple CS reconstructions. Practical experiments on fluorescence images demonstrate that even performing 10% of the measurements, the signal-to-noise ratio can be significantly improved while keeping reduced exposure time, preserving edges and the image sharpness.
authors.affiliation: Institut Pasteur, Unité d'Analyse d'Images Quantitative CNRS URA 2582, F-75015 Paris, France. marim@pasteur.fr
authors.forename: Marcio
authors.initials: M
authors.lastname: Marim
authors.forename: Elsa
authors.initials: E
authors.lastname: Angelini
authors.forename: Jean-Christophe
authors.initials: JC
authors.lastname: Olivo-Marin
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D044962:Data Compression / Q000379:methods*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique