Affiliation:
1. Chinese Academy of Sciences
2. University of Chinese Academy of Sciences
Abstract
In fluorescence microscopy, background blur and noise are two main factors preventing
the achievement of high-signal-to-noise ratio (SNR) imaging.
Background blur primarily emanates from inherent factors including the
spontaneous fluorescence of biological samples and out-of-focus
backgrounds, while noise encompasses Gaussian and Poisson noise
components. To achieve background blur subtraction and denoising
simultaneously, a pioneering algorithm based on low-frequency
background estimation and noise separation from high-frequency
(LBNH-BNS) is presented, which effectively disentangles noise from the
desired signal. Furthermore, it seamlessly integrates low-frequency
features derived from background blur estimation, leading to the
effective elimination of noise and background blur in wide-field
fluorescence images. In comparisons with other state-of-the-art
background removal algorithms, LBNH-BNS demonstrates significant
advantages in key quantitative metrics such as peak signal-to-noise
ratio (PSNR) and manifests substantial visual enhancements. LBNH-BNS
holds immense potential for advancing the overall performance and
quality of wide-field fluorescence imaging
techniques.
Funder
National Natural Science Foundation of China
Subject
Atomic and Molecular Physics, and Optics,Engineering (miscellaneous),Electrical and Electronic Engineering
Cited by
1 articles.
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