Gradient Domain Weighted Guided Image Filtering

TL;DR

This paper introduces a gradient domain weighted guided image filtering algorithm that effectively reduces halo artifacts, sharpens edges, and improves image denoising and detail enhancement by utilizing gradient and weighted information.

Contribution

It presents a novel guided filtering method that leverages gradient and weighted information to suppress halo artifacts and enhance image quality.

Findings

Significantly reduces halo artifacts in images

Produces sharper edges and less blurring in flat areas

Enhances image denoising and detail preservation

Abstract

Guided image filter is a well-known local filter in image processing. However, the presence of halo artifacts is a common issue associated with this type of filter. This paper proposes an algorithm that utilizes gradient information to accurately identify the edges of an image. Furthermore, the algorithm uses weighted information to distinguish flat areas from edge areas, resulting in sharper edges and reduced blur in flat areas. This approach mitigates the excessive blurring near edges that often leads to halo artifacts. Experimental results demonstrate that the proposed algorithm significantly suppresses halo artifacts at the edges, making it highly effective for both image denoising and detail enhancement.