Unmixing biological fluorescence image data with sparse and low-rank poisson regression

HIGHLIGHTS

• who: Bioinformatics et al. from the Department of Mathematics and Statistics, University at Albany, SUNY, Albany, NY, United States have published the Article: Unmixing biological fluorescence image data with sparse and low-rank Poisson regression, in the Journal: (JOURNAL)
• what: The authors propose a regularized sparse and low-rank Poisson regression unmixing approach (SL-PRU) to deconvolve spectral images labeled with highly overlapping fluorophores which are recorded in low signal-to-noise regimes. Third the authors propose a method to tune the SL-PRU parameters involved in the unmixing procedure in the absence of knowledge . . .
  

   

  

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