NDLI: Video denoising algorithm based on improved dual-domain filtering and 3D block matching

Content Provider	IET Digital Library
Author	Xiao, Jinsheng Zou, Wentao Zhang, Shangyue Lei, Junfeng Wang, Wen Wang, Yuan Fang
Abstract	This study introduces an algorithm for video denoising based on improved dual-domain filtering and 3D block matching. The wavelet thresholding based on 3D block matching is introduced to make full use of the correlation of video sequence in order to apply dual-domain filtering to the video. A layered approach is used that attempts denoising in both a base layer and a detail layer. The result of wavelet thresholding based on 3D block matching is used as a guide image to make the base layer smoother. Shrinkage of short-time Fourier transform coefficients further decreases the noise in the detail layer. Experimental results show that the authors’ algorithm generates a better base layer and detail layer than the traditional dual-domain filtering algorithms. The subjective and objective comparisons of different algorithms also prove that the proposed algorithm performs better for video denoising.
Starting Page	2250
Ending Page	2257
Page Count	8
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 12, Dec (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5563
Journal	IET Image Processing
Publisher Date	2018-08-29
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	3D Block Matching Computer Vision And Image Processing Technique Dual-domain Filtering Filtering Method in Signal Processing Fourier Transform Fourier Transforms Image Denoising Image Filtering Image Matching Image Recognition Image Segmentation Image Sequence Integral Transforms Stereo Image Processing Video Denoising Algorithm Video Sequence Video Signal Processing Wavelet Thresholding Wavelet Transform
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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