Tampere University of Technology

TUTCRIS Research Portal

Variance Stabilization for Noisy+Estimate Combination in Iterative Poisson Denoising

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Pages (from-to)1086-1090
Number of pages5
JournalIEEE Signal Processing Letters
Volume23
Issue number8
DOIs
Publication statusPublished - 1 Aug 2016
Publication typeA1 Journal article-refereed

Abstract

We denoise Poisson images with an iterative algorithm that progressively improves the effectiveness of variance-stabilizing transformations (VST) for Gaussian denoising filters. At each iteration, a combination of the Poisson observations with the denoised estimate from the previous iteration is treated as scaled Poisson data and filtered through a VST scheme. Due to the slight mismatch between a true scaled Poisson distribution and this combination, a special exact unbiased inverse is designed. We present an implementation of this approach based on the BM3D Gaussian denoising filter. With a computational cost at worst twice that of the noniterative scheme, the proposed algorithm provides significantly better quality, particularly at low signal-to-noise ratio, outperforming much costlier state-of-the-art alternatives.

Keywords

  • Anscombe transformation, image denoising, iterative filtering, photon-limited imaging, Poisson noise

Publication forum classification

Field of science, Statistics Finland

Downloads statistics

No data available