对传统双边滤波器模型中的灰度标准差和滤波窗口进行改进。首先,用固定大小的正方形窗口通过概率分布函数和最大似然函数计算图中每个像素点的噪声标准差,将全图噪声标准差的中值作为阈值,若某像素点的噪声标准差大于该阈值,则认为该点的窗口内包含图像边缘,用半边旋转窗口法重新计算该点的噪声标准差和滤波窗口;然后,对图像中的每个像素点进行双边滤波,其中,灰度标准差设为该点噪声标准差的2倍;最后,根据区域相似度模型判定强噪声,并利用中值滤波器去除。实验证明,所提算法在不同强度的噪声下均可取得较好的保边滤波效果和强噪声去除效果。

In this study, the gray standard deviation and filter window of the traditional bilateral filter model are improved. First, the noise standard deviation with respect to each pixel in a picture is calculated using the probability distribution and maximum likelihood functions in a fixed square window. Subsequently, the median of the noise standard deviation of the whole picture is considered to be the threshold value. The window of the pixel will contain an edge if the noise standard deviation of a pixel point is greater than the threshold value. Thus, the noise standard deviation and filter window of the pixel point are recalculated using the half-edge rotating window method. Then, each pixel point is filtered using a bilateral filter, where twice the noise standard deviation of this pixel point is considered to be the gray standard deviation. Finally, a strong noise can be judged based on the regional similarity model, and the median filter is used to eliminate the noise. The experiments denote that the edge-preserving and filtering performances of the proposed algorithm are excellent under different noise intensities, and the proposed algorithm can effectively eliminate the strong noise.