基于递归中值滤波的椒盐噪声去除的熵图SSIM分析
Entropy-map SSIM analysis of Salt and Pepper Noise Removal via Recursive Median Filterring
浏览论文内容
中文总结 AI 辅助
本研究提出SSIM-Map熵图指标，用于评估递归中值滤波去除椒盐噪声的效果，该指标对残留伪影更敏感，补充了传统SSIM-Img。
中文摘要 AI 辅助
本文研究了在递归阈值算法中使用中值滤波器（MF）去除灰度图像中的椒盐（SP）噪声。去噪性能使用两个互补的指标进行评估：SSIM-Img和SSIM-Map。SSIM-Img是标准的图像质量评估（IQA），即在恢复图像与干净图像之间计算传统的结构相似性指数（SSIM）。SSIM-Map是一种新颖的IQA，基于SSIM的评估，在图像的熵图之间计算，其中熵图通过滑动窗口中的奇异值分解熵获得。我们表明，SSIM-Map对残留脉冲伪影、模糊和局部强度过渡更为敏感，因此补充了传统的SSIM-Img指标。
英文摘要
This paper studies the removal of salt-and-pepper (SP) noise from grayscale images using a median filter (MF) within a recursive thresholding algorithm. Denoising performance is assessed using two complementary metrics: SSIM-Img and SSIM-Map.SSIM-Img is standard Image Quality Assessment (IQA), the conventional Structural Similarity Index (SSIM) computed between the restored and clean images. SSIM-Map is novel IQA, an SSIM-based evaluation computed between entropy maps of these images, where the maps are obtained using singular value-decomposition entropy in sliding windows. We show that SSIM-Map is more sensitive to residual impulse artifacts, blur, and local intensity transitions, and therefore complements the conventional SSIM-Img metric.
发表机构
- Petrozavodsk State University（彼得罗扎沃茨克国立大学）
机构由 AI 辅助整理，请以论文原文为准。
