A fast oversampling orthogonal method for the discrete two dimensional S-transform computing

Author(s):  
Guodong Zhang ◽  
Changchun Chen ◽  
Donghong Sun
Author(s):  
Priya R. Kamath ◽  
Kedarnath Senapati ◽  
P. Jidesh

Speckles are inherent to SAR. They hide and undermine several relevant information contained in the SAR images. In this paper, a despeckling algorithm using the shrinkage of two-dimensional discrete orthonormal S-transform (2D-DOST) coefficients in the transform domain along with shock filter is proposed. Also, an attempt has been made as a post-processing step to preserve the edges and other details while removing the speckle. The proposed strategy involves decomposing the SAR image into low and high-frequency components and processing them separately. A shock filter is used to smooth out the small variations in low-frequency components, and the high-frequency components are treated with a shrinkage of 2D-DOST coefficients. The edges, for enhancement, are detected using a ratio-based edge detection algorithm. The proposed method is tested, verified, and compared with some well-known models on C-band and X-band SAR images. A detailed experimental analysis is illustrated.


2017 ◽  
Vol 402 ◽  
pp. 1-8 ◽  
Author(s):  
Min Zhong ◽  
Feng Chen ◽  
Chao Xiao

Author(s):  
Yu-huan Luan ◽  
Feng-rong Sun ◽  
Paul Babyn ◽  
Shang-ling Song ◽  
Gui-hua Yao ◽  
...  

2017 ◽  
Vol 40 (7) ◽  
pp. 2387-2395 ◽  
Author(s):  
Yi Ji ◽  
Hong-Bo Xie

Time-frequency representiation has been intensively employed for the analysis of biomedical signals. In order to extract discriminative information, time-frequency matrix is often transformed into a 1D vector followed by principal component analysis (PCA). This study contributes a two-directional two-dimensional principal component analysis (2D2PCA)-based technique for time-frequency feature extraction. The S transform, integrating the strengths of short time Fourier transform and wavelet transform, is applied to perform the time-frequency decomposition. Then, 2D2PCA is directly conducted on the time-frequency matrix rather than 1D vectors for feature extraction. The proposed method can significantly reduce the computational cost while capture the directions of maximal time-frequency matrix variance. The efficiency and effectiveness of the proposed method is demonstrated by classifying eight hand motions using 4-channel myoelectric signals recorded in health subjects and amputees.


2017 ◽  
Vol 54 (4) ◽  
pp. 041206
Author(s):  
宋梦洒 Song Mengsa ◽  
陈文静 Chen Wenjing

2013 ◽  
Vol 51 (10) ◽  
pp. 1138-1142 ◽  
Author(s):  
Min Zhong ◽  
Wenjing Chen ◽  
Tao Wang ◽  
Xianyu Su

2017 ◽  
Vol 402 ◽  
pp. 430-436 ◽  
Author(s):  
Min Zhong ◽  
Feng Chen ◽  
Chao Xiao ◽  
Jiangping Zhu

2016 ◽  
Vol 54 (5) ◽  
pp. 3025-3034 ◽  
Author(s):  
Fei Gao ◽  
Xiangshang Xue ◽  
Jinping Sun ◽  
Jun Wang ◽  
Ye Zhang

Sign in / Sign up

Export Citation Format

Share Document