IEEE Trans Image Process - Edge-preserving image regularization based on morphological wavelets and dyadic trees.

Tópicos

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Resumo

Despite the tremendous success of wavelet-based image regularization, we still lack a comprehensive understanding of the exact factor that controls edge preservation and a principled method to determine the wavelet decomposition structure for dimensions greater than 1. We address these issues from a machine learning perspective by using tree classifiers to underpin a new image regularizer that measures the complexity of an image based on the complexity of the dyadic-tree representations of its sublevel sets. By penalizing unbalanced dyadic trees less, the regularizer preserves sharp edges. The main contribution of this paper is the connection of concepts from structured dyadic-tree complexity measures, wavelet shrinkage, morphological wavelets, and smoothness regularization in Besov space into a single coherent image regularization framework. Using the new regularizer, we also provide a theoretical basis for the data-driven selection of an optimal dyadic wavelet decomposition structure. As a specific application example, we give a practical regularized image denoising algorithm that uses this regularizer and the optimal dyadic wavelet decomposition structure.

Resumo Limpo

despit tremend success waveletbas imag regular still lack comprehens understand exact factor control edg preserv principl method determin wavelet decomposit structur dimens greater address issu machin learn perspect use tree classifi underpin new imag regular measur complex imag base complex dyadictre represent sublevel set penal unbalanc dyadic tree less regular preserv sharp edg main contribut paper connect concept structur dyadictre complex measur wavelet shrinkag morpholog wavelet smooth regular besov space singl coher imag regular framework use new regular also provid theoret basi datadriven select optim dyadic wavelet decomposit structur specif applic exampl give practic regular imag denois algorithm use regular optim dyadic wavelet decomposit structur

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