基于相关度预测的椒盐噪声自适应滤除算法

在分析现有的细节保护滤波算法的基础上,提出了一种基于相关度预测的图像椒盐噪声自适应滤除算法。对于信号像素,保持灰度值不变。对于噪声嫌疑像素,利用对邻域灰度相关量化分析和定义的灰度相关函数作为信号邻域相关性的度量,并将该系数作为预测滤波算法的阈值进行判别。根据像素被判定为噪声或有效信号的概率,自行调整滤波强度,减少图像滤波处理中的细节损失。实验表明,该算法的噪声滤除能力、细节保护能力以及运算效率都可以得到满意的结果。

Image Salt ＆ Pepper Noise Self-adaptive Suppression Algorithm with Correlativity and Prediction

Through summarizing the existing detail-preserving salt pepper noise suppression methods in the gray image,a new Correlativity and Prediction self-adaptive weighted algorithm is proposed.To the noise pixel,according to the correlations of the neighboring pixels and their characteristics,the algorithm sets adaptively prediction coefficients,while the signal pixels are kept untouched to preserve the detail of the image.It was based on the correlativity principle of image pixel gray.The neighboring correlativity coefficient was defined to identify noisy pixels and adaptively adjust the filtering intensity.Experiments show that the result of salt pepper noise suppression,detail-preserving and computation efficiency are satisfactory.