New adaptive median denoising model combined with cyclic iterative method

Abstract:

Aiming at the disadvantage of traditional salt-and-pepper noise removal methods that the denoising performance is poor when the image noise density is high, a new adaptive median denoising model combined with cyclic iterative method is proposed, in order to improve the performance of denoising algorithm in high-density salt and pepper removal. The working mode of the proposed filter can be divided into three stages. First, the image is preprocessed, that is, the suspected noise points are obtained from the pixels to be processed using the extreme value judgment method. Secondly, the noise points are determined and replaced adaptively by the median or mean value in the neighborhood to complete the denoising. Finally, the suspected noise points are processed again, and whether the suspected noise points are noise points is further judged by the algorithm with built-in parameters and conditions. The noise mark point method is also induced, and the filtered image is obtained by finding the end of mark point denoising through iterative processing. The results of simulation experiments show that the proposed method has a certain improvement in denoising performance for both low-density noise images and high-density noise images, compared with several traditional salt-and-pepper noise removal filtering algorithms.