ABSTRACT
Poisson noise (also known as shot or photon noise) arises due to the lack of information during the image acquisition phase, it is quite common in the field of microscopic or astronomical imaging applications. In this paper, we propose a non-local total variation regularization framework with a p-norm driven data-fidelity for denoising the Poissonian images. In precise, the energy functional is derived using a Maximum A Posteriori estimator of the Poisson probability density function. The diffusion amounts to a non-local total variation minimization process, which eventually preserves fine structures while denoising the data. The numerical solution is sought under a fast converging split-Bregman iterative scheme. The proposed model is compared visually and statistically with the state-of-the-art Poisson denoising models.
Acknowledgments
Mr. Shivarama Holla would like to thank the Ministry of Human Resource Development, Government of India, for providing the financial assistance to pursue Ph.D. research work at National Institute of Technology, Karnataka, India.
Disclosure statement
No potential conflict of interest was reported by the authors.