基于自适应秩聚类滤波器的图像去噪方法
Image Denoising via the Adaptive Rank-Cluster Filter
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中文总结 AI 辅助
本文提出自适应秩聚类滤波器，经Otsu聚类、模糊融合处理像素强度，在混合噪声场景下对图像去噪的鲁棒性优于多种基线滤波算法。
中文摘要 AI 辅助
本文提出一种空间局部图像去噪滤波器，并将其性能指标与基线滤波算法（包括中值滤波、自适应中值滤波、高斯滤波、双边滤波、维纳滤波、各向异性扩散滤波及非局部均值滤波）进行对比评估。该滤波器的核心思路为：先对3×3窗口内的像素按强度排序，经修剪后保留7个元素，再通过最优Otsu分割形成两个聚类，将中心像素的强度值与其中一个聚类的统计多数强度对齐；随后将计算得到的强度值与窗口内像素的中值强度进行模糊融合。实验结果表明，该滤波器对图像噪声水平的变化具有最高的鲁棒性，尤其在处理混合噪声时表现突出——混合噪声由椒盐脉冲噪声与不同比例的加性高斯噪声构成。
英文摘要
A spatial-local image-denoising filter is proposed, and its performance metrics are evaluated in comparison with baseline filtering algorithms, including the median, adaptive median, Gaussian, bilateral, Wiener, anisotropic diffusion, and non-local means. The developed filter is based on aligning the intensity value of the central pixel in a 3x3 window with the statistical majority intensity of one of the two clusters formed by optimal Otsu's partitioning of a pixel set sorted by intensity and trimmed to seven elements. This is followed by a fuzzy fusion of the calculated value with the median intensity of the pixels within the window. The proposed filter demonstrates the highest robustness to variations in image noise levels, particularly when processing mixed noise consisting of salt-and-pepper impulse noise and additive Gaussian noise in various proportions
