Weighted guided filter based on peak-aware and multi-scale constraints

doi:10.3785/j.issn.1008-973X.2024.10.008

Journal of ZheJiang University (Engineering Science)

2024, Vol. 58

Issue (10): 2053-2061 DOI: 10.3785/j.issn.1008-973X.2024.10.008

Weighted guided filter based on peak-aware and multi-scale constraints

Quan ZHANG(

),Haizhong LIU*(

)

College of Mathematics and Physics, Lanzhou Jiaotong University, Lanzhou 730070, China

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Abstract

A new weighted guided image filter was proposed for the problem that guided image filtering fails to preserve sharp edges and leads to excessive structural blurring in smoothed images. Peak-aware weighting was utilized to extract edge and structural information from images, and the robustness of the proposed filter was improved by multi-scale constraints. The regularization term coefficients of the filter loss function were improved to an adaptive form based on the image variance information. Application experiments were carried out in edge-aware smoothing, image detail enhancement, texture removal smoothing, and image denoising, and the results showed that the proposed filter outperformed the guided image filters involved in the comparison in terms of visualization, peak signal-to-noise ratio, and structural similarity. Compared with the peak signal-to-noise ratio and structural similarity of the suboptimal filters of the edge-aware smoothing experiments, the peak signal-to-noise ratio was 2.62 dB higher on average, and the structural similarity was 0.0286 higher on average.

Key words： guided filter image smoothing peak aware multi-scale constraint edge preserving

Received: 16 June 2023 Published: 27 September 2024

CLC:

TP 391

Fund: 甘肃省优秀研究生“创新之星”项目（2023CXZX-592）.

Corresponding Authors: Haizhong LIU E-mail: 12211601@stu.lzjtu.edu.cn;liuhzh@lzjtu.edu.cn

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Cite this article:

Quan ZHANG,Haizhong LIU. Weighted guided filter based on peak-aware and multi-scale constraints. Journal of ZheJiang University (Engineering Science), 2024, 58(10): 2053-2061.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2024.10.008 OR https://www.zjujournals.com/eng/Y2024/V58/I10/2053

基于峰值感知和多尺度约束的加权引导滤波器

引导图像滤波无法保留锐利边缘，导致平滑图像出现结构过度模糊问题, 为此提出新的加权引导图像滤波器. 利用峰值感知加权提取图像的边缘和结构信息，通过多尺度约束提高所提滤波器的鲁棒性. 基于图像方差信息将滤波损失函数的正则化项系数改进为自适应式. 边缘感知平滑、图像细节增强、纹理去除平滑以及图像去噪领域的应用实验结果表明, 所提滤波器在视觉效果、峰值信噪比以及结构相似度上均优于参与对比的引导图像滤波器. 与边缘感知平滑实验中次优滤波器的峰值信噪比和结构相似度相比，所提滤波器的峰值信噪比平均高2.62 dB, 结构相似度平均高0.0286.

关键词： 引导滤波器, 图像平滑, 峰值感知, 多尺度约束, 边缘保持

Fig.1 Comparison of model coefficient for different filters

Tab.1 Comparison of structural similarity for different filter models

Tab.2 Comparison of model performance before and after regularization term coefficient adaptation

Fig.2 Original of edge-aware smoothing experiment

Fig.3 Visual comparison of image smoothing results for different filter models

Tab.3 Comparison of image smoothing results for different filter models

Fig.4 Pixel intensity distribution of 180th horizontal profile for input image and six model filtered outputs

Fig.5 Visual comparison of image detail enhancement effect for different filter models

Fig.6 Visual comparison of texture removal effect for different filter models

Fig.7 Image for denoising test

Tab.4 Comparison of image denoising results for different filter models

Fig.8 Image for denoising visualization experiment

Fig.9 Visual comparison of image denoising results for different filter models


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