Weighted Anisotropic-Isotropic Total Variation for Poisson Denoising
Kevin Bui Yifei Lou Fredrick Park Jack Xin
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.