Locally analytic schemes: a link between diffusion filtering and wavelet shrinkage. (English) Zbl 1161.68831
Summary: We study a class of numerical schemes for nonlinear diffusion filtering that offers insights on the design of novel wavelet shrinkage rules for isotropic and anisotropic image enhancement. These schemes utilise analytical or semi-analytical solutions to dynamical systems that result from space-discrete nonlinear diffusion filtering on minimalistic images with \(2\times 2\) pixels. We call them locally analytic schemes and locally semi-analytic schemes, respectively. They can be motivated from discrete energy functionals, offer sharp edges due to their locality, are very simple to implement because of their explicit nature, and enjoy unconditional absolute stability. They are applicable to singular nonlinear diffusion filters such as TV flow, to bounded nonlinear diffusion filters of Perona-Malik type, and to tensor-driven anisotropic methods such as edge-enhancing or coherence-enhancing diffusion filtering.
The fact that these schemes use processes within \(2\times 2\)-pixel blocks allows to connect them to shift-invariant Haar wavelet shrinkage on a single scale. This interpretation leads to novel shrinkage rules for two- and higher-dimensional images that are scalar-, vector- or tensor-valued. Unlike classical shrinkage strategies they employ a diffusion-inspired coupling of the wavelet channels that guarantees an approximation with an excellent degree of rotation invariance. By extending these schemes from a single scale to a multi-scale setting, we end up at hybrid methods that demonstrate the possibility to realise the effects of the most sophisticated diffusion filters within a fairly simplistic wavelet setting that requires only Haar wavelets in conjunction with coupled shrinkage rules.
The fact that these schemes use processes within \(2\times 2\)-pixel blocks allows to connect them to shift-invariant Haar wavelet shrinkage on a single scale. This interpretation leads to novel shrinkage rules for two- and higher-dimensional images that are scalar-, vector- or tensor-valued. Unlike classical shrinkage strategies they employ a diffusion-inspired coupling of the wavelet channels that guarantees an approximation with an excellent degree of rotation invariance. By extending these schemes from a single scale to a multi-scale setting, we end up at hybrid methods that demonstrate the possibility to realise the effects of the most sophisticated diffusion filters within a fairly simplistic wavelet setting that requires only Haar wavelets in conjunction with coupled shrinkage rules.
MSC:
| 68U10 | Computing methodologies for image processing |
| 68T05 | Learning and adaptive systems in artificial intelligence |
| 65T60 | Numerical methods for wavelets |
| 60J70 | Applications of Brownian motions and diffusion theory (population genetics, absorption problems, etc.) |
| 60G35 | Signal detection and filtering (aspects of stochastic processes) |
Keywords:
image processing; diffusion filtering; wavelet shrinkage; finite difference methods; dynamical systems; stability; rotation invarianceReferences:
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