IEEE Trans Image Process - Autocorrelation-driven diffusion filtering.

Tópicos

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Resumo

In this paper, we present a novel scheme for anisotropic diffusion driven by the image autocorrelation function. We show the equivalence of this scheme to a special case of iterated adaptive filtering. By determining the diffusion tensor field from an autocorrelation estimate, we obtain an evolution equation that is computed from a scalar product of diffusion tensor and the image Hessian. We propose further a set of filters to approximate the Hessian on a minimized spatial support. On standard benchmarks, the resulting method performs favorable in many cases, in particular at low noise levels. In a GPU implementation, video real-time performance is easily achieved.

Resumo Limpo

paper present novel scheme anisotrop diffus driven imag autocorrel function show equival scheme special case iter adapt filter determin diffus tensor field autocorrel estim obtain evolut equat comput scalar product diffus tensor imag hessian propos set filter approxim hessian minim spatial support standard benchmark result method perform favor mani case particular low nois level gpu implement video realtim perform easili achiev

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