Unfortunately, completely free is impossible! To prepare for a good audio result, you should check the microphone's noise specifications. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.When we use a microphone, we expect the cleanest possible signal, ideally completely free of annoying noise. (M–N) Denoised images of the subregion marked in (A) using NLM and PBF, respectively. (H–L) Denoised images of the subregion marked in (A) for the five different values of g = σ/ α 2tested other parameters used are the same as in the experiments on synthetic image data. (G) Manual identification of EB1 particles for the subregion marked in (A), arrowheads indicate the locations of particles, green: strong particle yellow: weak particle red: particle not discernible. (F) ROC curves comparing filtering performance (see main text). (D–E) Resultant images after denoising (A) by NLM filter (D) and PBF (E). (C) Resultant image after denoising (A) by FP-NLM filter with λ = 10.0 (based on the 20% selection criterion), h = 0.9 σ, g = σ/70 and σ = 20. (B) Good SNR image: same time-point as (A) after average projection of 3 Z-planes (see main text). (A) Low SNR image: single time-point, single Z-plane. All rights reserved.Īpplication of denoising to the detection of EB1 foci in a Drosophila egg chamber expressing EB1-GFP. We show that our feature-preserving non-local means filter can reduce the threshold of imaging conditions required to obtain meaningful data.Ĭopyright © 2010 Elsevier Inc. We have applied this approach to live-cell images resulting in enhanced detection of end-binding-protein 1 foci on dynamically extending microtubules in photo-sensitive Drosophila tissues. We show that this filter achieves higher peak signal-to-noise ratio in denoised images and has a greater capability in identifying weak particles when applied to synthetic data. The particle probability image is then used to improve the estimation of the correct coefficients for averaging. Our adaptive method addresses this problem by constructing a particle feature probability image, which is based on Haar-like feature extraction. The commonly used non-local means filter is not optimal for noisy biological images containing small features of interest because image noise prevents accurate determination of the correct coefficients for averaging, leading to over-smoothing and other artifacts. We present a novel feature-preserving non-local means approach to denoise such images to improve feature recovery and particle detection. Fluorescence imaging of dynamical processes in live cells often results in a low signal-to-noise ratio.
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