为了在去除图像噪声的同时最大程度地保持图像细节，提出了一种基于模糊推理的噪声检测及自适应滤波方法。该方法首先利用图像的局部统计信息（ROAD）和方向Laplacian差分值，同时采用模糊推理的方法对噪声点进行检测；然后对可能的噪声点进行自适应的滤波处理，使非噪声点的原有灰度保持不变，以最大程度地保持图像的真实性；最后针对由图像中噪声分布不均产生的局部噪声密度较高和高的噪声图像而进行的模糊推理噪声检测可能引起的“误判点”，设计了一种改进的滤波方案用来对其进行修正。该方案采用迭代思想来重复进行噪声定位和滤波，每次滤波只滤除较大可能的噪声点，以最大限度减小图像的模糊。实验结果表明，该方案对于不同程度的噪声，经过适当地迭代均可取得良好的去噪效果。

In order to preserve image fine details while de-noising,a new noise detection and adaptive filter method,which is based on fuzzy reasoning technique,was proposed.At first,according to the local statistic information ROAD(rank-order absolute differences) and orientational laplacian differences,the possible noises was detected with fuzzy reasoning technique.Then possible noise was filtered with an adaptive method,which can preserve details to a great extent by keeping uncontaminated pixels unchanged.Lastly,some possible error judged noise caused by local high noise intensity and high noise image were corrected with an improved filtering approach，in which relatively possible noise was located and filtered iteratively,each time only the most possible noise was filtered.Experimental results show that with appropriate iterative the proposed method is efficient for different noise intensities.