基于不确定性信息融合的高密度椒盐噪声降噪方法

A Novel Algorithm for Removing High-Density Salt-and-Pepper Noise Based on Fusion of Indeterminacy Information

为解决高密度椒盐噪声滤除与细节保护之间的矛盾,提出一种基于不确定性信息融合的中智灰滤波算法.该算法包括两个阶段：噪声检测和噪声恢复.在检测阶段,为提高噪声检测准确率,首先利用Max-Min算法进行初测,然后利用极值压缩灰色关联度与顺序不确定性的融合信息进行二次判断.在噪声恢复阶段,为充分利用像素本身的不确定性及邻域像素的灰色关联性,将中智不确定性和极值压缩灰色关联度的乘积作为相似性度量特征,设计了中智灰自适应权重函数.实验表明,针对不同图像,二次噪声检测方案的噪声剔除率可达0.1%-8.8%;该中智灰滤波算法在抑制椒盐噪声的同时能较好地保护图像边缘信息,特别是在高噪声（70%-90%）情况下,算法的综合性能优于现有相关算法.

To solve the contradiction of image denoising and detail-preserving under high-density salt-and-pepper noise, this paper proposes a Neutrosophy-Gray filter by using the fusion of indetelminacy information. It has a two-stage scheme： noise detecting and noise removing. In detecting stage,to improve the accuracy of noise detection,Max-Min algorithm is em- ployed firstly, then noise candidates are judged again by the dual criteria of Extreme-Compression-Grey-Correlation-Degree （ECGCD） and Ordered - Indeterminacy （OI）. In filtering stage, the algorithm applies the multiplicative fusion of ECGCD and indeterminacy to measure the similarity of pixels, and a Neutrosophy-Gray adaptive weighted function is presented. Experi- ments show, for different images, the rates of noise eliminating change between 0. 1% and 8. 8%, and performances of denois- ing and detail-preserving of the proposed algorithm are superior to current filters even at high level noise （70% - 90% ）.