Real-time image denoising of mixed Poisson-Gaussian noise in fluorescence microscopy images using ImageJ

Varun Mannam, Yide Zhang, Yinhao Zhu, Evan Nichols, Qingfei Wang, Vignesh Sundaresan, Siyuan Zhang, Cody Smith, Paul W. Bohn, Scott S. Howard

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Fluorescence microscopy imaging speed is fundamentally limited by the measurement signal-to-noise ratio (SNR). To improve image SNR for a given image acquisition rate, computational denoising techniques can be used to suppress noise.However, common techniques to estimate a denoised image froma single frame either are computationally expensive or rely on simple noise statistical models. These models assume Poisson or Gaussian noise statistics, which are not appropriate for many fluorescence microscopy applications that contain quantum shot noise and electronic Johnson- Nyquist noise, therefore a mixture of Poisson andGaussian noise. In this paper, we show convolutional neural networks (CNNs) trained on mixed Poisson and Gaussian noise images to overcome the limitations of existing image denoising methods. The trained CNN is presented as an open-source ImageJ plugin that performs real-time image denoising (within tens of milliseconds) with superior performance (SNR improvement) compared to conventional fluorescence microscopy denoising methods. The method is validated on external datasets with out-of-distribution noise, contrast, structure, and imaging modalities fromthe training data and consistently achieves high-performance (>8 dB) denoising in less time than other fluorescence microscopy denoising methods.

Original languageEnglish (US)
Pages (from-to)335-345
Number of pages11
Issue number4
StatePublished - Apr 2022
Externally publishedYes

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics


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