Image Scale-Space Filtering Using Directional Local Variance Controlled Anisotropic Diffusion

Abstract

The purpose of this paper is to develop an effective edge indicator and propose an image scale-space filter based on anisotropic diffusion equation for image denoising. We first develop an effective edge indicator named directional local variance (DLV) for detecting image features, which is anisotropic and robust and able to indicate the orientations of image features. We then combine two edge indicators (i.e., DLV and local spatial gradient) to formulate the desired image scale-space filter and incorporate the modulus of noise magnitude into the filter to trigger time-varying selective filtering. Moreover, we theoretically show that the proposed filter is robust to the outliers inherently. A series of experiments are conducted to demonstrate that the DLV metric is effective for detecting image features and the proposed filter yields promising results with higher quantitative indexes and better visual performance, which surpass those of some benchmark models.