Open AccessOnline burr video denoising by learning sparsifying transform
Author(s) -
Han Lili,
Li Shujuan,
Liu Xiuping,
Guo Jiaan
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.6243
Subject(s) - noise reduction , image denoising , computer science , artificial intelligence , video denoising , computer vision , pattern recognition (psychology) , speech recognition , video processing , video tracking , multiview video coding
The burrs on high‐voltage copper contact leads to point discharge and device damage. Since the high‐voltage copper contact has different machining batch and the contour various which the machine tool remove is not economic and robot usually is used to remove the burrs of high‐voltage copper contact. The first step for robot deburring is to identify burrs. In order to improve the performance of copper contact burr video denoising, this article presents online burr video denoising sparsifying transforms algorithm, which defined two alternative values between the optimal sparse signal and transform learning dictionary, simultaneously, calculated the mean of peak signal‐to‐noise ratio, the mean of execution time, the STD (STandard Deviation), and the VAR (VARiance), accordingly presented an burr video denoising algorithm and compared with state‐of‐the‐art video denoising algorithms. The experiment results show that compared with traditional methods, the burr video denoising algorithm has higher denoising precision, faster denoising speed, and stronger high‐noise‐level processing capacity, and so on. The numeric experiments show that the proposed approach has higher peak signal‐to‐noise ratio and less computation complexity than the existing video denoising methods.