Research On Application Of Adaptive Mathematical Morphology In Image Denoising And Edge Detection

Mathematical morphology, as an emerging image processing theory, is widely used in various fields of image processing because of its solid theoretical foundation and simple algorithm. A structure element with fixed shape and value is always used by the traditional mathematical morphology, and it may leads to a bad influence to the processing result if it is inappropriate. The theory of adaptive mathematical morphology, which can select an appropriate structure element based on the local feature of an input image, is studied in this paper and some discussion is made to prove its advantages during the image denoising and edge-detection.The theory of gray mathematical morphology is firstly studied and the drawbacks of the traditional mathematical morphology which uses a fixed structure element in image denoising and edge detection are analyzed in this paper. Then two adaptive mathematical morphology methods which can select structure elements adaptively are proposed respectively to solve these drawbacks. The effectiveness of the proposed methods is proved through simulation experiments, and the results of the experiments show that the adaptive morphology methods not only solve the drawbacks of traditional morphology methods but also have good processing results.In order to extend the adaptive morphology method for gray image denoising to the color image field, the features of color image and some different color vector ordering methods are studied in this paper. Then the proposed adaptive morphology method for gray image denoising is respectively extended to the color image field through two vector ordering methods--reduced ordering method and marginal ordering method. Experiment results show that both of the proposed methods based on the two ordering methods have good denoising effect.