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Poisson Noise Reduction in Deconvolution Microscopy

doi: 10.6062/jcis.2011.02.03.0044(Free PDF)


Murillo R.P. Homem, Marcelo R. Zorzan and Nelson D.A. Mascarenhas


Computational optical sectioning microscopy is a powerful tool to reconstruct three-dimensional images from optical two-dimensional sections of a biological specimen acquired by means of a fluorescence microscope. Due to limiting factors in the imaging systems, the images are degraded by both the optical system and detection process. Each of the two-dimensional section of the three-dimensional data set are blurred by contributions of light from other out-of-focus planes. Besides, they are also corrupted by noise due to quantum fluctuations of light. In this work we present a method to perform the restoration of three-dimensional data obtained by fluorescence microscopy. The algorithm consists of the use of a noise reduction procedure based on the Anscombe transformation followed by the Richardson-Lucy deconvolution algorithm. Results showed an improvement on deconvolution performance when using phantoms and real cell images


computational data analysis and simulation in general sciences, computational optical sectioning microscopy, deconvolution microscopy, poisson noise, Anscombe transformation.


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