Perceptual Color Image Smoothing via a New Region-Based PDE Scheme

In this paper, we present a new color image regularization method using a rotating smoothing filter. This approach combines a pixel classification method, which roughly determines if a pixel belongs to a homogenous region or an edge with an anisotropic perceptual edge detector capable of computing two precise diffusion directions. Using a now classical formulation, image regularization is here treated as a variational model, where successive iterations of associated PDE (Partial Differential Equation) are equivalent to a diffusion process. Our model uses two kinds of diffusion: isotropic and anisotropic diffusion. Anisotropic diffusion is accurately controlled near edges and corners, while isotropic diffusion is applied to smooth regions either homogeneous or corrupted by noise. A comparison of our approach with other regularization methods applied on real images demonstrate that our model is able to efficiently restore images as well as handle diffusion, and at the same time preserve edges and corners well

Approximate Orientation Steerability Based on Angular Gaussians

Junctions are signicant features in images with intensity variation that exhibits multiple orientations. This makes the detection and characterization of junctions a challenging problem. The characterization of junctions would ideally be given by the response of a lter at every orientation. This can be achieved by the principle of steerability that enables the decomposition of a lter into a linear combination of basis functions. However, current steerability approaches suer from the consequences of the uncertainty principle: In order to achieve high resolution in orientation they need a large number of basis lters increasing, thus, the computational complexity. Furthermore, these functions have usually a wide support which only accentuates the computational burden. In this paper we propose a novel alternative to current steerability approaches. It is based on utilizing a set of polar separable lters with small support to sample orientation information. The orientation signature is then obtained by interpolating orientation samples using Gaussian functions with small support. Compared with current steerability techniques our approach achieves a higher orientation resolution with a lower complexity. In addition, we build a polar pyramid to characterize junctions of arbitrary inherent orientation scales. Index terms: Low-level vision, steerable lters, orientation analysis. Final Manuscript (IP 3032) Submitted to IEEE Trans. on Image Processing EDICS number: 2-ANAL (image analysis) 1