An Improved Guided Filtering Algorithm for Image Enhancement

Abstract

Guided image filter (GIF) is popular in image processing and computer vision for the properties of edge-preserving and low computational complexity, but GIF may suffer from over-smoothing (halo artifacts) near sharp edges and under-smoothing at flat regions. There is a tradeoff between them in the original cost function of GIF. In this paper, an improved guided filter (IGIF) is proposed by incorporating an adaptive structure aware constraint. The adaptive structure aware constraint can well preserve edges and smooth details through assigning different weights to different local structure. Simultaneously, thanks to the L 1 penalty, the proposed IGIF can exactly remove small details at the flat regions. To illustrate the effectiveness of the proposed IGIF, we apply it to image enhancement. Experimental results show that the proposed filter can produce enhanced images with better visual quality as well as quantitative performance.