A Study On Denoising Algorithm Of Gaussian Noise In A Subregional Image

In the process of acquisition and transmission,the image is often destroyed by noise.In order to make the image processing work smoothly,eliminate the interference of noise to the image information,reduce the influence of image noise on image processing,and image restoration is indispensable.In order to solve this problem,the researchers propose a number of solutions,and the original image is one of the many image restoration methods.In this paper,the problem of image gaussian noise denoising is studied in this paper,and some improvements are made to the traditional algorithm,which is better than the traditional algorithm.According to the above analysis,based on the traditional Canny edge detection algorithm based on improved,based on the theory of improved edge detection algorithm for image segmentation,according to the results of research and analysis,put forward a new algorithm of image gaussian noise: research the algorithms of image gaussian noise,image segmentation and image edge detection algorithm,after analysis compared the BM3 D filtering,wiener filtering and mean filtering denoising effect and characteristics of three kinds of algorithms.Based on the above analysis results,based on the improved edge detection algorithm,the image segmentation for the smooth region and edges region,according to the experimental results obtained in the region of the image edge using BM3 D filtering,the noise in the image smooth region by wiener,average hybrid filter for image denoising.Compared with traditional image gaussian noise algorithm,this algorithm possesses.In view of the above analysis,a new image Gauss noise algorithm is proposed in this paper.After studying the image Gauss noise algorithm,image segmentation and image edge detection algorithm,the denoising effect and special point of the three algorithms,BM3 D filtering,Wiener filtering and mean filtering,are analyzed and compared.Based on the above analysis results,the image is divided into smooth region and edge region based on the improved edge detection algorithm.BM3 D filtering is used to denoise in the edge region of the image based on the experimental results,and the image denoising is taken by Wiener and mean mixed filter in the image smooth region.Compared with the traditional image Gauss noise algorithm,the algorithm has better denoising effect,and also can preserve the edge details of the image.The simulation experiment verifies that the improved image edge detection algorithm can obtain better image edge detection effect.The segmentation algorithm based on improved image edge detection algorithm has excellent image segmentation capability.Based on the above two studies of the dividing area denoising method can retain the image edge detail at the same time,get better peak signal-to-noise ratio also can obtain good image visual effect.