Poisson denoising with multiple directional lots
Zhiyu Chen, Shogo Muramatsu · 2014
This paper proposes a Poisson denoising with a union of directional lapped orthogonal transforms (DirLOTs). DirLOTs are 2-D non-separable lapped orthogonal transforms with directional characteristics. Its bases overcome a disadvantage of the separable wavelet image denoising for the diagonal textures and edges. Based on this feature, multiple DirLOTs are used to improve the performance by introducing redundant representation with multiple directions. Experimental results show the combination of the variance stabilizing transformation (VST), Stein's unbiased risk estimator-linear expansion of thresholds (SURE-LET) approach and multiple DirLOTs is able to significantly improve the denoising performance, and verify the feasibility of the proposed method.