A Variational Approach to Reconstructing Images Corrupted by Poisson Noise

Authors:
Triet Le;Rick Chartrand;Thomas J. Asaki
Affiliations:
Department of Mathematics, Yale University, New Haven 06520-8283;Los Alamos National Laboratory, Theoretical Division, Los Alamos 87545;Los Alamos National Laboratory, Computer and Computational Sciences Division, Los Alamos 87545
Venue:
Journal of Mathematical Imaging and Vision
Year:
2007

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Abstract

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.