提出递归阈值中值滤波与自编码器结合方法，有效去除椒盐噪声并评估图像模糊。
Recursive Threshold Median Filter and Autoencoder for Salt-and-Pepper Denoising: SSIM analysis of Images and Entropy Maps
- 用递归阈值中值滤波和三层自编码器处理椒盐噪声，分阶段优化恢复效果。
- 中值滤波在50%-60%强噪声下仍有效，自编码器仅适用于<30%低噪声场景。
- 引入熵图结构相似性新指标，更好捕捉模糊和局部过渡，适合参数调优。
本文研究基于中值滤波（MF）与简单三层自编码器（AE）在递归阈值算法中去除图像椒盐噪声的方法。采用两个指标评估去噪性能：标准结构相似性指数 SSIMImg（恢复图像与干净图像对比）及新提出的 SSIMMap——通过滑动窗二维样本熵计算的图像熵图结构相似性。实验表明，SSIMMap对模糊和局部强度变化更敏感，可补充 SSIMImg。在低、高分辨率灰度图像上测试显示，递归阈值中值滤波在50%-60%强噪声下仍具鲁棒性，而简单自编码器仅能处理<30%低噪声。本文提出两种可扩展方案：(i) 2MF 使用两个不同窗口大小的中值滤波加最终阈值化，适合低分辨率下突出锐利细节；(ii) MFs-AE 通过自编码器聚合多个中值滤波特征，有利于高分辨率下整体场景结构恢复。由于其简洁性和计算效率，中值滤波更适合资源受限平台（边缘/IoT），而自编码器在无预去噪时表现不佳。结果验证了 SSIMMap 在客观模糊评估与去噪参数调优中的实用价值。
原文摘要 · Abstract (English)
This paper studies the removal of salt-and-pepper noise from images using median filter (MF) and simple three-layer autoencoder (AE) within recursive threshold algorithm. The performance of denoising is assessed with two metrics: the standard Structural Similarity Index SSIMImg of restored and clean images and a newly applied metric SSIMMap - the SSIM of entropy maps of these images computed via 2D Sample Entropy in sliding windows. We shown that SSIMMap is more sensitive to blur and local intensity transitions and complements SSIMImg. Experiments on low- and high-resolution grayscales images demonstrate that recursive threshold MF robustly restores images even under strong noise (50-60 %), whereas simple AE is only capable of restoring images with low levels of noise (<30 %). We propose two scalable schemes: (i) 2MF, which uses two MFs with different window sizes and a final thresholding step, effective for highlighting sharp local details at low resolution; and (ii) MFs-AE, which aggregates features from multiple MFs via an AE and is beneficial for restoring the overall scene structure at higher resolution. Owing to its simplicity and computational efficiency, MF remains preferable for deployment on resource-constrained platforms (edge/IoT), whereas AE underperforms without prior denoising. The results also validate the practical value of SSIMMap for objective blur assessment and denoising parameter tuning.
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