Title :
Autocorrelation-Driven Diffusion Filtering
Author :
Felsberg, Michael
Author_Institution :
Dept. of Elec trical Eng., Linkoping Univ., Linkoping, Sweden
fDate :
7/1/2011 12:00:00 AM
Abstract :
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.
Keywords :
adaptive filters; coprocessors; correlation methods; image denoising; image enhancement; iterative methods; tensors; video signal processing; GPU implementation; anisotropic diffusion; autocorrelation driven diffusion filtering; autocorrelation estimation; diffusion tensor field; evolution equation; image autocorrelation function; iterated adaptive filtering; video real time performance; Anisotropic magnetoresistance; Approximation methods; Correlation; Equations; Kernel; Noise reduction; Tensile stress; Adaptive filtering; diffusion filtering; image enhancement; steerable filters; structure tensor;
Journal_Title :
Image Processing, IEEE Transactions on
DOI :
10.1109/TIP.2011.2107330