Suppression of Multiplicative Noise Based on Adaptive Windowing and Local Structure Detection

Multiplicative noise makes the interpretation of image extremely difficult, and the fixed-size window filters cannot achieve good trade-off between noise suppression and edge keeping. Based on adaptive windowing and local structure detection, a new filtering algorithm of multiplicative noise is developed in this paper. The sliding window size is automatically adjusted by adaptive windowing, and the local structure detection is required for each window determined because appearance of point target and edge feature cannot satisfy the basic premise of stationary in increment for all statistical filters. Point target is preserved to keep edges and fine details, and the most homogeneous semiwindow on which the central pixel lies is chosen by gradient masks to enhance noise reduction in edge areas. The denoising experiments demonstrate that the proposed filter is superior both in noise suppression and in fine detail preserving.