Research On Image Denoising Algorithms Based On Improved BM3D

The rapid development of science and technology has spawned the information revolution.Digital image is an important media in the process of acquiring,expressing and transmitting information,so its accuracy and clarity are essential,however,noise always leads to image degradation.In order to obtain high-quality images,the researches about image denoising algorithm have never stopped.Among existing algorithms,Block-matching and 3D filtering?BM3D?is the best one,but it still has some shortcomings including low efficiency,poor denoising result of high-intensity noise image and image detail information lost.In order to get more ideal denoising image,it is urgent and important to improve the original BM3D algorithm and propose a more effective one.This paper first makes researches on SFCM image clustering algorithm and adaptive edge detection algorithm.Then based on these work,this paper makes a valuable improvement on the BM3D algorithm Finally this paper illustrates the advantages of the improved algorithm over the traditional one by simulation experiments.The main innovations and research results in this paper are as follows:?1?To solve the problems of the BM3D algorithm that it has low efficiency due to global search and poor denoising result of high-intensity noise image,an improved BM3D algorithm based on SFCM clustering is proposed.With the gray-scale distribution characteristics of image pixels,the SFCM divides image pixels into different categories,each of which is a homogeneous region.The pixels in a homogeneous region are more similar,while the pixels in a non-homogeneous region are far less similar.Then the blocking-match can be limited to the homogeneous region rather than performing global search.Resulting from it,the efficiency of the improved algorithm is higher than the original one.Because of the SFCM's insensitive to noise,the high-intensity noise images can acquire more accurate image blocks,and with the help of the improved weighted ₂L-norm that calculates the similarity between two image blocks,the high-intensity noise images can get better denoising results.A large number of experimental results show that the improved algorithm improves the PSNR of images by an average of 0.95dB,especially of the high-intensity noise image by 1.28dB,and the algorithm running time by an average of 4.81s.?2?To solve the other problem of the BM3D algorithm that image loses edge and texture information after denoising,an improved BM3D algorithm based on adaptive edge detection is proposed.With the image structure character,the adaptive edge detection divides the whole image into smooth area and edge area.In the edge area,block-matching is performed along the edge direction,which can help preserve edge and texture information while getting better denoising results.A large number of experimental results show that the improved algorithm improves the PSNR of images by an average of 0.80dB and the SSIM of images by an average of 1.11 times.?3?Combining?1?and?2?,an image denoising algorithm based on image segmentation is proposed.The algorithm divides the whole image into homogeneous region and edge region,then performs block-matching respectively.The algorithm gets better image denoising results,preserves more edge and texture information and improve the quality of high-intensity noise image a lot.On this basis,the efficient color noise image denoising comes into being realized.A lot of experimental results show that the improved algorithm improves the PSNR of gray images by an average of 1.77dB,the SSIM by 1.10 times,and reduces the algorithm running time by 4.21s,and improves the PSNR of color images by average of0.58dB,the SSIM by 1.05 times,and reduces the algorithm running time by 3.78s.In addition,compared with famous classical image denoising algorithms,the proposed algorithm performs best for both grayscale and color images.