Improved Adaptive Bilateral Filtering Algorithm

In this study, the gray standard deviation and filter window of the traditional bilateral filter model arc 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 arc 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 arc excellent under different noise intensities, and the proposed algorithm can effectively eliminate the strong noise.