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Image Processing with Deconvolution

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Author: Lauren Alvarenga
Wednesday, January 01, 2020
Olympus America Inc., Life Science

In an effort to improve the contrast and sharpness of images captured with light microscopes, deconvolution has become a mainstay in the field of microscopy due to its ability to computationally achieve these goals without the need for additional optics or confocal pinholes. This white paper explains the differences between popular deconvolution techniques such as deblurring, inverse filtering, and constrained iterative methods to help determine which algorithms best suit your imaging needs.

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File: Image_Processing_with_Deconvolution.pdf (589.04 KB)
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deconvolutionContrastdeblurringinverse filteringconfocalsuper-resolutionconstrained iterative
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