自适应加权修正里斯均值滤波器有效去除高密度椒盐噪声
Adaptive Weight Modified Riesz Mean Filter For High-Density Salt and Pepper Noise Removal
- 基于像素权重与自适应机制，融合改进的里斯均值滤波思想
- 在60%-95%噪声密度下，PSNR与SSIM均优于现有先进滤波器
- 适合图像去噪任务，尤其对高密度椒盐噪声场景优化显著
本文提出一种新型滤波器——自适应加权修正里斯均值滤波器（AWMRmF），用于高效去除高密度椒盐噪声（SPN）。该方法结合像素权重函数和源自自适应修正里斯均值滤波器（DAMRmF）的自适应条件。在26幅典型测试图像上，评估了AWMRmF与自适应频率中值滤波器（AFMF）、自适应加权均值滤波器（AWMF）、自适应塞萨罗均值滤波器（ACmF）、自适应里斯均值滤波器（ARmF）及改进自适应加权均值滤波器（IAWMF）的性能，噪声水平覆盖60%至95%。实验结果表明，在峰值信噪比（PSNR）和结构相似性（SSIM）指标上，AWMRmF均优于其他主流滤波器，且在平均PSNR与平均SSIM方面表现更优。
原文摘要 · Abstract (English)
This paper introduces a novel filter, the Adaptive Weight Modified Riesz Mean Filter (AWMRmF), designed for the effective removal of high-density salt and pepper noise (SPN). AWMRmF integrates a pixel weight function and adaptivity condition inspired by the Different Adaptive Modified Riesz Mean Filter (DAMRmF). In my simulations, I evaluated the performance of AWMRmF against established filters such as Adaptive Frequency Median Filter (AFMF), Adaptive Weighted Mean Filter (AWMF), Adaptive Cesaro Mean Filter (ACmF), Adaptive Riesz Mean Filter (ARmF), and Improved Adaptive Weighted Mean Filter (IAWMF). The assessment was conducted on 26 typical test images, varying noise levels from 60% to 95%. The findings indicate that, in terms of both Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) metrics, AWMRmF outperformed other state-of-the-art filters. Furthermore, AWMRmF demonstrated superior performance in mean PSNR and SSIM results as well.
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