Visible light video denoising and FPGA hardware implementation

被引:0
|
作者
Zhao S. [1 ,2 ]
Wan M. [1 ,2 ]
Qian W. [1 ,2 ]
Zhou L. [1 ,2 ]
Shao A. [1 ,2 ]
Chen Q. [1 ,2 ]
Gu G. [1 ,2 ]
机构
[1] School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing
[2] Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing
来源
Guangxue Jingmi Gongcheng/Optics and Precision Engineering | 2022年 / 30卷 / 15期
关键词
FPGA; Gaussian difference filtering; image processing; motion compensation; video denoising; visible light;
D O I
10.37188/OPE.20223015.1868
中图分类号
学科分类号
摘要
It is difficult to suppress the noise in static state of the existing filtering algorithm. Moreover, motion compensated filtering algorithm fails to effectively suppress noise. To solve these problems, a video denoising algorithm based on spatio-temporal filtering is proposed and implemented on the field programmable gate array(FPGA). The algorithm mainly uses Gaussian difference filtering to extract image features, and then applies spatial filtering to suppress high-frequency noise. Simultaneously, different denoising strategies are adopted for the segmented image area by feedback. Implementing hardware requires high-level synthesis tools to simplify programming, and is to make DDR3 control module to operate input and output of video stream between modules. Simulation results show that the proposed algorithm can be used for denoising. For different scenes, the peak signal-to-noise ratio can be increased by up to 15 dB in comparison with the denoising algorithm based on a non-subsampled contourlet(NSCT). After transplanting the algorithm to FPGA, the difference between PSNR and MATLAB simulation program was approximately 0. 3 dB, and the running time was shortened by over 71. 5%. Considering the real-time performance, PSNR achieves a better visible video denoising effect. © 2022 Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码:1868 / 1879
页数:11
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