Thresholding-Based Image Denoising Using Two-Dimensional S-Transform

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.

Download Full-text

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

2009 Asia-Pacific Conference on Computational Intelligence and Industrial Applications (PACIIA) ◽

10.1109/paciia.2009.5406570 ◽

2009 ◽

Author(s):

Guodong Zhang ◽

Changchun Chen ◽

Donghong Sun

Keyword(s):

Two Dimensional ◽

Orthogonal Method ◽

S Transform

Download Full-text

A frequency-based window width optimized two-dimensional S-Transform profilometry

Optics Communications ◽

10.1016/j.optcom.2017.05.011 ◽

2017 ◽

Vol 402 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Min Zhong ◽

Feng Chen ◽

Chao Xiao

Keyword(s):

Two Dimensional ◽

Window Width ◽

S Transform

Download Full-text

Myoelectric signal classification based on S transform and two-directional two-dimensional principal component analysis

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331217704035 ◽

2017 ◽

Vol 40 (7) ◽

pp. 2387-2395 ◽

Cited By ~ 1

Author(s):

Yi Ji ◽

Hong-Bo Xie

Keyword(s):

Principal Component Analysis ◽

Feature Extraction ◽

Computational Cost ◽

Principal Component ◽

Component Analysis ◽

Two Dimensional ◽

Time Frequency ◽

Frequency Decomposition ◽

S Transform ◽

Efficiency And Effectiveness

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.

Download Full-text

PD pattern recognition using S transform and two-directional two-dimensional principal component analysis

2012 International Conference on High Voltage Engineering and Application ◽

10.1109/ichve.2012.6357134 ◽

2012 ◽

Author(s):

Gaolin Wu ◽

Ruilin Xu ◽

Ke Wang ◽

Qian Wang ◽

Yan Yang ◽

...

Keyword(s):

Principal Component Analysis ◽

Pattern Recognition ◽

Principal Component ◽

Component Analysis ◽

Two Dimensional ◽

S Transform

Download Full-text

A Review on Image Denoising Techniques

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.39342 ◽

2021 ◽

Vol 9 (12) ◽

pp. 761-764

Author(s):

Smita Vasant Tempe

Keyword(s):

Image Denoising ◽

Visual Information ◽

Filter Banks ◽

Geometrical Structure ◽

Two Dimensional ◽

Sampled Data ◽

Discrete Domain ◽

Continuous Domain

Abstract: The goal of this study is to find a "genuine" two-dimensional transform that can capture the fundamental geometrical structure that is important in visual information. The discontinuous character of the data is the most difficult aspect of analysing geometry in photographs. Unlike previous approaches, such as curvelets, which generate a transform in the continuous domain and then discretize for sampled data, we begin with a discrete-domain construction and then investigate its convergence to a continuous-domain expansion. We use nonseparable filter banks to create a discrete-domain multiresolution and multidirection expansion, similar to how wavelets are produced from filter banks. As a result of this construction, a flexible multiresolution, local, and directed picture expansion employing contour segments is obtained, and it is therefore useful.

Download Full-text