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To make up for the deficiency, a weighted guided image filter (WGIF) was proposed recently by incorporating an edge-aware weighting into the filtering process. It takes the advantages of local and global operations, and achieves better performance in edge

This paper proposes a novel gradient-weighted guided filtering algorithm to address the limitations of existing guided image filter and its derived versions, namely GIF, WGIF, GDGIF, Xie and WAGIF.

In this paper, a weighted guided image filter with entropy evaluation weighting (EEW-WGIF) is proposed by introducing the weighting strategy based on entropy evaluation method (EEM) and incorporating an explicit edge-aware constraint based on the gradient

We propose a weighted-based side window gradient guided filtering (WSGGF), built upon GGF. In WSGGF, both regression and adaptive regularization terms are improved upon side window framework to achieve better edge-preserving performance.

前言这是我《Guided Filtering的开端与发展》专栏的第二章，本章主要写的是带有权重的引导滤波。 这个系列都是以Guided Filtering为中心讲述的引导滤波后续的研究进展。