Weighted Anisotropic-Isotropic Total Variation for Poisson Denoising

TL;DR

This paper introduces a novel Poisson denoising model using weighted anisotropic-isotropic total variation regularization, combined with an efficient optimization algorithm, achieving superior image quality and computational performance.

Contribution

The paper proposes a new Poisson denoising approach with weighted AITV regularization and an efficient ADMM-based algorithm, improving upon existing methods.

Findings

Outperforms existing Poisson denoising methods in image quality

Achieves higher computational efficiency

Effectively preserves important image details

Abstract

Poisson noise commonly occurs in images captured by photon-limited imaging systems such as in astronomy and medicine. As the distribution of Poisson noise depends on the pixel intensity value, noise levels vary from pixels to pixels. Hence, denoising a Poisson-corrupted image while preserving important details can be challenging. In this paper, we propose a Poisson denoising model by incorporating the weighted anisotropic-isotropic total variation (AITV) as a regularization. We then develop an alternating direction method of multipliers with a combination of a proximal operator for an efficient implementation. Lastly, numerical experiments demonstrate that our algorithm outperforms other Poisson denoising methods in terms of image quality and computational efficiency.