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Combined Forward and Backward Anisotropic Diffusion Filtering of Color Images
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Reconstruction of Wavelet Coefficients Using Total Variation Minimization
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Bridging scale-space to multiscale frame analyses
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Interpreting translation-invariant wavelet shrinkage as a new image smoothing scale space
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Forward-and-backward diffusion processes for adaptive image enhancement and denoising
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From two-dimensional nonlinear diffusion to coupled Haar wavelet shrinkage
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Correspondence between frame shrinkage and high-order nonlinear diffusion
Applied Numerical Mathematics
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ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
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We study the connections between discrete one-dimensional schemes for nonlinear diffusion and shift-invariant Haar wavelet shrinkage. We show that one step of a (stabilised) explicit discretisation of nonlinear diffusion can be expressed in terms of wavelet shrinkage on a single spatial level. This equivalence allows a fruitful exchange of ideas between the two fields. In this paper we derive new wavelet shrinkage functions from existing diffusivity functions, and identify some previously used shrinkage functions as corresponding to well known diffusivities. We demonstrate experimentally that some of the diffusion-inspired shrinkage functions are among the best for translation-invariant multiscale wavelet denoising. Moreover, by transferring stability notions from diffusion filtering to wavelet shrinkage, we derive conditions on the shrinkage function that ensure that shift invariant single-level Haar wavelet shrinkage is maximum---minimum stable, monotonicity preserving, and variation diminishing.