A Variational Approach to Reconstructing Images Corrupted by Poisson Noise

• DOI: 10.1007/s10851-007-0652-y
• Publication Date: 2007-03-30T20:58:16Z
• Authors: Triet Le; Rick Chartrand; Thomas J. Asaki
• Keywords: 0101 mathematics; 01 natural sciences

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.