SURE-LET Poisson Denoising with Multiple Directional LOTs

Zhiyu CHEN  Shogo MURAMATSU  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E98-A   No.8   pp.1820-1828
Publication Date: 2015/08/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E98.A.1820
Type of Manuscript: PAPER
Category: Image
Keyword: 
Poisson noise,  multiple DirLOTs,  wavelet shrinkage,  anscombe transform,  SURE-LET,  

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Summary: 
This paper proposes a Poisson denoising method with a union of directional lapped orthogonal transforms (DirLOTs). DirLOTs are 2-D non-separable lapped orthogonal transforms with directional characteristics under the fixed-critically-subsampling, overlapping, orthonormal, symmetric, real-valued and compact-support property. In this work, DirLOTs are used to generate symmetric orthogonal discrete wavelet transforms and then a redundant dictionary as a union of unitary transforms. The multiple directional property is suitable for representing natural images which contain diagonal textures and edges. Multiple DirLOTs can overcome a disadvantage of separable wavelets in representing diagonal components. In addition to this feature, multiple DirLOTs make transform-based denoising performance better through the redundant representation. Experimental results show that the combination of the variance stabilizing transformation (VST), Stein's unbiased risk estimator-linear expansion of threshold (SURE-LET) approach and multiple DirLOTs is able to significantly improve the denoising performance.