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中文摘要：
现有的图像椒盐噪声滤除算法缺乏对小于滤波窗口的图像细节与边缘信息的保护能力，本文提出了一种基于二次噪声检测和细节保护规则函数的图像椒盐噪声滤波算法，算法将滤噪过程分为两个阶段：噪声检测和噪声恢复阶段．在噪声检测过程中，用自适应中值原理对图像中的噪声点进行初步检测，然后通过局部模糊隶属度函数对检测出的噪声点进行二次判断，有效提高了噪声检测的准确度．在噪声恢复阶段，利用细节保护规则函数与岛数据逼近的凸面代价函数来恢复噪声点．为了充分利用图像局部特征，该算法自适应地选择噪声点周围的象素点利用细节规则保护函数得到输出值，当图像噪声点的凸面代价函数值达到最小时，噪声图像得到最佳恢复．实验结果表明，本文提出的滤波算法针对椒盐噪声具有很好的细节保护与噪声滤除能力，特别是在噪声感染率高（70％以上）的情况下，算法性能优于现有的其它算法．
英文摘要：
The major drawback of recent image filtering algorithms for removing impulse noise is lack of the ability of preserving the image details and edges which are smaller than the size of filtering windows. To alleviate this limitation, a new image filtering algorithm using a double noise detector and edge-preserving regularizafion function is proposed in this paper. The proposed filter has a two-stage scheme： detecting noise and removing noise. In order to improve accurate rate of noise detection, noise candidates identified with the noise detection algorithm of the adaptive median filter are judged again by local fuzzy membership function, and then a convex objective function composed of Q1 data-fidelity term and edge-preserving regularization function is employed to deal with noise candidates. The input of edge-preserving regularizafion function is adaptively selected to take full advantage of local features of the image. The image corrupted by noise is restored successfully as the convex objective function gets its minimum. Experimental results show the superiority of the proposed filter in terms of the ability of removing noise and preserving the details and edges of the image in comparison with some recent methods,it is also shown that even at a very high noise level （ 〉 70% ） details and edges of the original image are preserved very well with our method.
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