NDLI: Improved BM3D denoising method

Content Provider	IET Digital Library
Author	Li, Ying Jiang Zhang, Jiangwei Wang, Maoning
Abstract	Block matching 3D denoising (BM3D) is an excellent single-image denoising method. However, it still needs to be improved for solving practical problems. In this study, the authors attempt to improve the method of BM3D. First, one of the problems of BM3D is that some of its references cannot perform self-adaption when the noise intensity of the images is changed. Therefore, they propose a method using total variation (TV) to calculate the image noise intensity and make the references perform self-adaption. Second, finding similar blocks in the BM3D method is a time-consuming procedure. To solve this problem, they analyse the relationship between the numbers of similar blocks and denoising effect, improve the process of searching for similar blocks, and reduce the running time. Third, through the experiment they find that the denoising effect of BM3D method in the domain of complex texture is unsatisfactory. Thus, they proposed a hybrid denoising method for the complex texture area, using the new TV model and BM3D method together to restore the image. Their experimental results show that the improved BM3D method performs better than the original BM3D method.
Starting Page	1197
Ending Page	1204
Page Count	8
ISSN	17519659
Volume Number	11
e-ISSN	17519667
Issue Number	Issue 12, Dec (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/11/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2016.1110
Journal	IET Image Processing
Publisher Date	2017-07-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Denoising Effect Hybrid Denoising Method Image Denoising Image Noise Intensity Calculation Image Restoration Image Texture Improved Block Matching 3D Denoising Method Improved BM3D Denoising Method Optical, Image And Video Signal Processing Running Time Reduction Single-image Denoising Method Total Variation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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