Autocorrelation-Driven Diffusion Filtering - Felsberg, Michael (Linköping University, 2018)
The dataset consists of Matlab code and 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.

The dataset was originally published in DiVA and moved to SND in 2024.

The dataset consists of Matlab code and 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.

Datasetet har ursprungligen publicerats i DiVA och flyttades över till SND 2024.
Autocorrelation-Driven Diffusion Filtering
Keywords: autonomous systems; autonoma system; Computer graphics and computer vision; Datorgrafik och datorseende