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This paper is dedicated to a new anisotropic diffusion approach for image regularization based on a gradient and two diffusion directions obtained from half Gaussian kernels. This approach results in smoothing an image while preserving edges. From an anisotropic edge detector, built of half Gaussian derivative kernels, we introduce a new smoothing method preserving structures which drives the diffusion function of the angle between the two edge directions and the gradient value. Due to the two directions diffusion used in the control function, our diffusion scheme enables to preserve edges and corners, contrary to other anisotropic diffusion methods. Moreover, parameters of the Gaussian kernel can be tuned to be sufficiently thin extracting precisely edges whereas its length allows detecting in contour orientations which leads to a coherent image regularization. Finally, we present some experimental results and discuss about the choice of the different parameters.

Evolution of image regularization with PDEs toward a new anisotropic smoothing based on half kernels