Journal of Mathematical Imaging and Vision

, Volume 27, Issue 3, pp 257–263 | Cite as

A Variational Approach to Reconstructing Images Corrupted by Poisson Noise

  • Triet Le
  • Rick Chartrand
  • Thomas J. Asaki


We propose a new variational model to denoise an image corrupted by Poisson noise. Like the ROF model described in [1] and [2], the new model uses total-variation regularization, which preserves edges. Unlike the ROF model, our model uses a data-fidelity term that is suitable for Poisson noise. The result is that the strength of the regularization is signal dependent, precisely like Poisson noise. Noise of varying scales will be removed by our model, while preserving low-contrast features in regions of low intensity.


image reconstruction image processing image denoising total variation Poisson noise radiography 


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Copyright information

© Springer Science + Business Media, LLC 2007

Authors and Affiliations

  1. 1.Department of MathematicsYale UniversityNew Haven
  2. 2.Los Alamos National Laboratory, Theoretical DivisionLos Alamos
  3. 3.Los Alamos National Laboratory, Computer and Computational Sciences DivisionLos Alamos

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