自适应聚类滤波在混合噪声下表现更优，尤其对椒盐与高斯噪声组合有效。
Image Denoising via the Adaptive Rank-Cluster Filter
- 基于3×3窗口内像素强度排序与奥茨最优分割，动态聚类并匹配中心像素
- 在不同噪声比例下均优于中值、双边、非局部均值等传统滤波器
- 融合模糊逻辑提升鲁棒性，适合处理复杂混合噪声图像
提出一种空间局部图像去噪滤波器，与中值、自适应中值、高斯、双边、维纳、各向异性扩散及非局部均值等基准算法进行对比。该滤波器基于将3×3窗口中心像素的灰度值与经最优奥茨分割后由7个裁剪像素构成的两簇中统计多数灰度值对齐，并结合窗口内像素的中值进行模糊融合。实验表明，该方法在处理不同比例的椒盐脉冲噪声与加性高斯噪声混合时具有最高鲁棒性。
原文摘要 · Abstract (English)
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
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。