Feature Preserving Image Smoothing Using a Continuous Mixture of Tensors

Many computer vision and image processing tasks require the preservation of local discontinuities, terminations and bifurcations. Denoising with feature preservation is a challenging task and in this paper, we present a novel technique for preserving complex oriented structures such as junctions and corners present in images. This is achieved in a two stage process namely, (1) All image data are preprocessed to extract local orientation information using a steerable Gabor filter bank. The orientation distribution at each lattice point is then represented by a continuous mixture of Gaussians. The continuous mixture representation can be cast as the Laplace transform of the mixing density over the space of positive definite (covariance) matrices. This mixing density is assumed to be a parameterized distribution, namely, a mixture of Wisharts whose Laplace transform is evaluated in a closed form expression called the Rigaut type function, a scalar-valued function of the parameters of the...

• Type: Conference
• Year: 2007
• Where: ICCV
• Authors: Özlem N. Subakan, Bing Jian, Baba C. Vemuri, C. Eduardo Vallejos
• Keywords: Computer Vision, Continuous Mixture Representation, Feature Preserving Denoising, ICCV 2007, Laplace Transform, Rigaut Type Function, Steerable Gabor Filter