A Novel Video Denoising Method Based on Surfacelet Transform

2008 ◽  
Author(s):  
Jingwen Yan ◽  
Hongzhi Xiao ◽  
Xiaobo Qu
Keyword(s):  
Denoising Method ◽  
Video Denoising ◽  
Surfacelet Transform

Study on Ground Penetrating Radar Signal Denoising Method Based on Surfacelet Transform and 3D Context Model

2014 ◽  
Vol 1010-1012 ◽  
pp. 1272-1275
Author(s):  
Dan Dan Liu ◽  
Zhi Qiu Yang ◽  
Chun Rui Tang
Keyword(s):  
Ground Penetrating Radar ◽  
Three Dimensional ◽  
Radar Signal ◽  
Signal Denoising ◽  
Context Model ◽  
Denoising Method ◽  
Subsurface Environment ◽  
Surface Singularities ◽  
Surfacelet Transform ◽  
Ground Penetrating

The ground penetrating radar and radar wave propagation in the subsurface environment is very complex. All kinds of noise and clutter interference is very serious, and detection echo data is a variety of with clutter. Therefore, the key techniques of data processing is to suppress clutter processing of ground penetrating radar record data. Surfacelet transform can efficiently capture and represent local surface singularities with different sizes. In order to improve the reliability of 3D ground penetrating radar detection results and accuracy, this paper presents a three-dimensional ground penetrating radar signal denoising method based on Surfacelet transform. Using Surfacelet transform and 3D context model for ground penetrating radar (GPR) analog signal to denoising, the noise in the case of low signal noise ratio (SNR) still can obtain a better result, and the simulations prove the effectiveness of the method.

scholarly journals Video Denoising Based on a Spatiotemporal Kalman-Bilateral Mixture Model

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Chenglin Zuo ◽  
Yu Liu ◽  
Xin Tan ◽  
Wei Wang ◽  
Maojun Zhang
Keyword(s):  
Mixture Model ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Bilateral Filter ◽  
Block Matching ◽  
Low Light ◽  
Denoising Method ◽  
Video Denoising ◽  
Temporal Domain ◽  
Video Frames

We propose a video denoising method based on a spatiotemporal Kalman-bilateral mixture model to reduce the noise in video sequences that are captured with low light. To take full advantage of the strong spatiotemporal correlations of neighboring frames, motion estimation is first performed on video frames consisting of previously denoised frames and the current noisy frame by using block-matching method. Then, current noisy frame is processed in temporal domain and spatial domain by using Kalman filter and bilateral filter, respectively. Finally, by weighting the denoised frames from Kalman filtering and bilateral filtering, we can obtain a satisfactory result. Experimental results show that the performance of our proposed method is competitive when compared with state-of-the-art video denoising algorithms based on both peak signal-to-noise-ratio and structural similarity evaluations.

scholarly journals Kalman Filtering Technique For Video Denoising Method

2012 ◽  
Vol 43 (20) ◽  
pp. 10-13
Author(s):  
Lakshmanan. S ◽  
Mythili.C Mythili.C ◽  
V. Kavitha
Keyword(s):  
Kalman Filtering ◽  
Denoising Method ◽  
Video Denoising ◽  
Filtering Technique
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