Research On Image Denoising Algorithm Based On BM3D

With the rapid development of technology,the digital image has been an important media for us to gain and deliver information.However,acquired images often contain noises or other kinds of false information,due to the low precision of the image collection equipment or the transmission system.To address this issue,image denoising algorithm was then proposed to acquire more reliable and precise information of images.Among the various existing image denoising algorithms,Block-matching and 3D filtering algorithm(BM3D)is well known to be one of the best algorithms for image denoising,but still has several problems.For example,in the era of big data,there is an urgent need for efficient image denoising algorithms to process mass image data,however BM3 D denoising algorithm is time-consuming due to its high computation complexity.In addition,as the scene of images becomes more and more complicated,the image contains more and more edge information.The BM3 D algorithm ignores the feature of these detail information and lacks the corresponding matching algorithms to different detail information of the images.To address the aforementioned problems and disadvantages,the main contributions of the thesis are as follows:(1)To address the issue of the high computation complexity and low efficiency of the BM3 D algorithm,a new BM3 D algorithm for image denoising based on image clustering is proposed.The novelties are as follows: 1)To further improve the efficiency of image denoising,we first cluster the pixels according to the feature of image on the basis of the fact that the pixels in homogeneous region usually have higher similarity.Then,the image blocks in homogenous region have higher matching score and in non-homogenous have lower matching score.Finally,we limit the search range within the homogenous region.2)To reduce the effect of the redundant information in the image blocks and the computation complexity of 3D transformation,a new irregular reference block matching method is proposed.Experimental results demonstrate that the running time of the proposed algorithm is 20.549 seconds less than that of existing methods,the denoising speed is 1.21 times faster than that of existing methods.Meanwhile the PSNR is increased averagely by 0.278 d B.(2)To address the poor denoising performance the BM3 D algorithm at the edges of the images,a novel BM3 D image denoising algorithm based on the edge detection is promoted.The novelty of the proposed method is as follow: we use edge detection algorithm to extract the edge of the image and search matching blocks along the edge contour,to improve the performance of image denoising at edges.Experimental results indicate that the PSNR of the proposed algorithm is enhanced by 0.49 d B at the edges of the images or in the regions containing detail information.(3)Based on the aforementioned advantages,a new image denoising algorithm combining image clustering and edge detection is promoted.This algorithm processes the edge and the non-edge region in parallel,which can keep the edges' texture and other detail information,and improve the efficiency of image denoising.Experimental results demonstrate that the promoted algorithm outperforms the original BM3 D algorithm on both of subjective visual renderings and objective quantitative evaluations.Specifically,compare with the original BM3 D algorithm,the PSNR of the proposed algorithm improves 0.716 d B on average and 1.031 d B in maximum.The running time of our algorithm is 19.962 seconds less than the original one,and the denoising speed is 1.2 times faster than that of the original one.