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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- 2.L. I. Rudin and S. Osher, “Total variation based image restoration with free local constraints,” in ICIP (1), pp. 31–35, 1994.Google Scholar
- 3.M. Green, “Statistics of images, the TV algorithm of Rudin-Osher-Fatemi for image denoising and an improved denoising algorithm,” CAM Report 02-55, UCLA, October 2002.Google Scholar
- 5.D. Donoho, “Nonlinear wavelet methods for recovery of signals, densities and spectra from indirect and noisy data,” in Proceedings of Symposia in Applied Mathematics: Different Perspectives on Wavelets, American Mathematical Society, 1993, pp. 173–205.Google Scholar
- 8.C. Kervrann and A. Trubuil, “An adaptive window approach for poisson noise reduction and structure preserving in confocal microscopy,” in International Symposium on Biomedical Imaging (ISBI’04), Arlington, VA, April 2004.Google Scholar
- 9.E. Jonsson, C.-S. Huang, and T. Chan, “Total variation regularization in positron emission tomography,” CAM Report 98-48, UCLA, November 1998.Google Scholar
- 13.X. Wu, R. Wang, and C. Wang, “Regularized image restoration based on adaptively selecting parameter and operator,” in 17th International Conference on Pattern Recognition (ICPR’04), Cambridge, UK, August 2004, pp. 602–605.Google Scholar