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<p>In response to the limitations of traditional infrared image enhancement algorithms, such as inadequate representation of detail layers and noise introduced during contrast enhancement, we propose a novel method based on Weighted Guided Image Filtering with Steering Kernel (SKWGIF). This method utilizes SKWGIF in conjunction with Gaussian Filtering (GF) to decompose the infrared image into a base layer that preserves edges and a detail layer rich in textures. We then apply Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the base layer, while the filter coefficients are used to enhance the detail layer and fuse the two layers. The images processed using the proposed algorithm demonstrate improved edge sharpness, enhanced texture details, and consistent contrast enhancement, all while effectively suppressing image noise. The code for this method is available at: <a href="https://github.com/XuZeDong-xzd/SKWGIF-DDE">https://github.com/XuZeDong-xzd/SKWGIF-DDE</a>.</p>

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Infrared Image Enhancement Using Weighted Guided Image Filtering with Steering Kernel

  • 期刊论文

  • 发表日期：

    2025年5月27日

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  • Zedong Xu,
  • Jianfeng Zhang

摘要

In response to the limitations of traditional infrared image enhancement algorithms, such as inadequate representation of detail layers and noise introduced during contrast enhancement, we propose a novel method based on Weighted Guided Image Filtering with Steering Kernel (SKWGIF). This method utilizes SKWGIF in conjunction with Gaussian Filtering (GF) to decompose the infrared image into a base layer that preserves edges and a detail layer rich in textures. We then apply Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the base layer, while the filter coefficients are used to enhance the detail layer and fuse the two layers. The images processed using the proposed algorithm demonstrate improved edge sharpness, enhanced texture details, and consistent contrast enhancement, all while effectively suppressing image noise. The code for this method is available at: https://github.com/XuZeDong-xzd/SKWGIF-DDE.