Extended Guided Image Filtering For Contrast Enhancement

Abstract

Guided image filtering (GIF) has been widely adopted in computer-vison tasks for properties of effective edge-preservation and low complexity. However, GIF is prone to suffer from halo artifacts near edges. Some new versions of GIF such as WGIF and GGIF employ various edge-aware regularizations to address this problem. But above local variance-based versions are sensitive to over-smoothing problem. The main reason is that local variance is not an adequate metric to discriminate the anisotropic image components. In this paper, we propose an extended GIF (EGIF) by incorporating a novel edge-aware factor into the regularization term. The new factor in entropy-variance domain not only can well discriminate details and edges but also have a good anti-noise ability. This helps EGIF to extend to industry applications. Experimental results of contrast enhancement show that EGIF can provide better visual quality as well as quantitative performance.