Image Statistics and Anisotropic Diffusion

Authors:
Hanno Scharr;Michael J. Black;Horst W. Haussecker
Affiliations:
-;-;-
Venue:
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Year:
2003

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Abstract

Many sensing techniques and image processing applicationsare characterized by noisy, or corrupted, image data.Anisotropic diffusion is a popular, and theoretically wellunderstood, technique for denoising such images. Diffusionapproaches however require the selection of an "edgestopping" function, the definition of which is typically adhoc. We exploit and extend recent work on the statisticsof natural images to define principled edge stopping functionsfor different types of imagery. We consider a varietyof anisotropic diffusion schemes and note that they computespatial derivatives at fixed scales from which we estimatethe appropriate algorithm-specific image statistics. Goingbeyond traditional work on image statistics, we also modelthe statistics of the eigenvalues of the local structure tensor.Novel edge-stopping functions are derived from these imagestatistics giving a principled way of formulating anisotropicdiffusion problems in which all edge-stopping parametersare learned from training data.