椒盐噪声图像的非局部平均滤波算法

针对非局部平均(NLM)方法对椒盐噪声图像滤波效果较差的问题,通过引入噪声检测结果扩展NLM方法去除图像中椒盐噪声。在噪声检测阶段,利用图像的两个极值Lmin和Lmax把图像像素点分为非噪声点和噪声点。在滤波阶段,非噪声点的灰度值保持不变。对于噪声点,如果以该噪声点为中心的自适应滤波窗口内均为噪声点,则认为该噪声点位于图像自身灰度值为Lmin或Lmax的区域内,使用两个极值的统计结果进行恢复。否则,采用改进的NLM方法滤除噪声。构造联合噪声检测模板避免噪声点对相似权计算的干扰,噪声点的恢复值由非噪声点的灰度值加权平均得到。此外,采用迭代滤波策略对高密度噪声图像噪声点进行恢复。相关去噪实验结果证实了算法去噪的有效性,不足之处是算法的时间复杂度较高。

A nonlocal means filter for images with salt-and-pepper noise

According to the fact that the nonlocal means (NLM) method cannot adequately remove noise from the images corrupted by salt-and-pepper noise, we extend the NLM to remove salt-and-pepper noise by introducing the noise detection results. At the noise detection stage, we divide pixels into two categories: noisy and noise-free pixels, depending on two extreme values Lmin and Lmax. At the filtering stage, noise-free pixels remain unchanged, while for each noise pixel, if the adaptive filtering window does not contain any noise-free pixel, we regard the current noise pixel located in image uniform regions composed of noise-free pixels with the same gray value Lmin or Lmax. And then the calculated statistics is used as the restored value. Otherwise, we employ the improved NLM filter for noise removal. The joint noise detection mask in the proposed method can avoid the influence of noise pixels on calculating similar weights in the presence of noise pixels, and only noise-free pixels are used for the weighted average. In addition, the iterative filtering scheme is used to remove noise of high-density. Experimental results demonstrate the effectiveness of the proposed filter even though its computational complexity is still high.