A Variational Approach to Reconstructing Images Corrupted by Poisson Noise
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We propose a new variational model to denoise an image corrupted by Poisson noise. Like the ROF model described in  and , 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.
Keywordsimage reconstruction image processing image denoising total variation Poisson noise radiography
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