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• W1662977547 abstract "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." @default.
• W1662977547 created "2016-06-24" @default.
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• W1662977547 date "2015-01-01" @default.
• W1662977547 modified "2023-09-25" @default.
• W1662977547 title "SURE-LET Poisson Denoising with Multiple Directional LOTs" @default.
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• W1662977547 doi "https://doi.org/10.1587/transfun.e98.a.1820" @default.
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