Gaussian highpass guided image filtering

• URL: http://arxiv.org/abs/2503.03284v1
• Date: Wed, 05 Mar 2025 09:12:12 GMT
• Title: Gaussian highpass guided image filtering
• Authors: Lei Zhao, Chuanjiang He,
• Abstract summary: We introduce a Prior Model based on Gaussian (highpass/lowpass) Filtering (PM-GF)<n>In the PM-GF, the guidance structure determined by Gaussian highpass filtering is obviously transferred to the filtering output.<n>We propose several Gaussian highpass GIFs (GH-GIFs) based on the PM-GF by emulating the original GIF and some improvements, i.e., using PM-GF instead of LAM in these GIFs.
• Score: 6.207147299773745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Guided image filtering (GIF) is a popular smoothing technique, in which an additional image is used as a structure guidance for noise removal with edge preservation. The original GIF and some of its subsequent improvements are derived from a two-parameter local affine model (LAM), where the filtering output is a local affine transformation of the guidance image, but the input image is not taken into account in the LAM formulation. In this paper, we first introduce a single-parameter Prior Model based on Gaussian (highpass/lowpass) Filtering (PM-GF), in which the filtering output is the sum of a weighted portion of Gaussian highpass filtering of the guidance image and Gaussian smoothing of the input image. In the PM-GF, the guidance structure determined by Gaussian highpass filtering is obviously transferred to the filtering output, thereby better revealing the structure transfer mechanism of guided filtering. Then we propose several Gaussian highpass GIFs (GH-GIFs) based on the PM-GF by emulating the original GIF and some improvements, i.e., using PM-GF instead of LAM in these GIFs. Experimental results illustrate that the proposed GIFs outperform their counterparts in several image processing applications.

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