用高斯高通引导图像滤波，更好保留边缘结构。
Gaussian highpass guided image filtering
- 引入单参数先验模型，结合高通与平滑滤波
- 在多类图像任务中性能优于传统方法
- 适合需要精细边缘保持的应用场景
引导图像滤波（GIF）是一种常用平滑技术，利用额外图像作为结构引导以去噪并保留边缘。原始GIF及其部分改进基于双参数局部仿射模型（LAM），但未考虑输入图像。本文提出一种基于高斯（高通/低通）滤波的单参数先验模型（PM-GF），其中输出为引导图像高斯高通滤波的加权部分与输入图像高斯平滑的叠加。该模型能更清晰地体现结构传递机制。在此基础上，我们提出多种基于PM-GF的高斯高通引导滤波器（GH-GIFs），通过替换原模型中的LAM实现。实验表明，所提方法在多个图像处理应用中表现优于原有版本。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。