Comparison of adaptive filtering in time-, transform- and frequency-domain: An electrogastrographic study

1994 ◽  
Vol 22 (4) ◽  
pp. 423-431 ◽  
Author(s):  
Jian De Z. Chen ◽  
Zhiyue Lin
Keyword(s):  
Frequency Domain ◽  
Adaptive Filtering

scholarly journals Speckle reduction in SAR images based on an adaptive filtering in the frequency domain

2020 ◽  
Vol 12 (2) ◽  
pp. 1-18
Author(s):  
Hamid Jannati ◽  
Mohammad Javad Valadan Zoej
Keyword(s):  
Frequency Domain ◽  
Adaptive Filtering ◽  
Speckle Reduction ◽  
Sar Images

Global Image Dehazing via Frequency Perception Filtering

2019 ◽  
Vol 28 (09) ◽  
pp. 1950142
Author(s):  
Linli Xu ◽  
Jing Han ◽  
Tian Wang ◽  
Lianfa Bai
Keyword(s):  
Frequency Domain ◽  
Adaptive Filtering ◽  
Low Frequency ◽  
Intermediate Frequency ◽  
Human Visual Perception ◽  
Image Dehazing ◽  
Weighting Factors ◽  
Outdoor Scenes ◽  
Frequency Domain Filtering ◽  
Three Components

In outdoor scenes, haze limits the visibility of images, and degrades people’s judgement of the objects. In this paper, based on an assumption of human visual perception in frequency domain, a novel image haze removal filtering is proposed. Combining this assumption with the theory of frequency domain filtering, we first estimate the cut-off frequency to divide the frequency domain of the hazy image into three components — low-frequency domain, intermediate-frequency domain and high-frequency domain. Then, by introducing the weighting factors, the three components are recombined together. After the theoretical deduction of frequency domain, the establishment of the actual model and adjusting the cut-off frequency and weighting factors, we finally acquire a global and adaptive filtering. This filtering can restore the details and the contours of the images, which have less noise, and improve the visibility of the objects in hazy images. Moreover, our method is simple in structure and strongly applicable, and rarely affected by parameters. Our algorithm is stable and performs well in heavy fog and the scene changes.

A sparsity-based adaptive filtering approach to Shear Wave Splitting

2021 ◽  
Author(s):  
Hamzeh Mohammadigheymasi ◽  
Mohammad Reza Ebrahimi ◽  
Graça Silveira ◽  
David schlaphorst
Keyword(s):  
Frequency Domain ◽  
Shear Wave ◽  
Adaptive Filtering ◽  
Elastic Anisotropy ◽  
Real Data ◽  
Shear Wave Splitting ◽  
Transverse Component ◽  
Time Frequency ◽  
Regularized Inversion ◽  
Wave Splitting

<p>Shear wave splitting analysis is a frequently used tool to study elastic anisotropy from the lower mantle to the crust. Several methods have been developed to evaluate the splitting parameters, Φ (fast axis) and δt (delay time), including the correlation of wave components, minimization of covariance matrix eigenvalues, and minimizing energy on the transverse component. Despite massive progress in introducing sophisticated methods, still fundamental problems, related mainly to noisy data, interfering phases, length of the analyzed waveform, and stability and reliability of results, remain. This study presents a sparsity-based adaptive filtering method to magnify the SKS waveforms and suppress the unwanted noise and interfering phases. The study is an extension of Jurkevics (1988), computing the semi-minor and semi-minor axis of the polarized motion in the time-frequency domain using a regularized inversion-based approach imposing a sparsity constraint. Afterward, the elliptical particle motion caused by the split shear waves and correspond to high semi-minor amplitude is derived in the time-frequency domain. The information is used to design an adaptive filter in the time domain to amplify the SKS phase and suppress the noise and other phases having non-elliptical polarization. The regularized inversion-based approach enables obtaining a sparse time-frequency semi-minor map while handling noise problems in the time-frequency decomposition. Conducting synthetic simulations, we show that the proposed method increases the signal-to-noise ratio of the SKS phase in radial and transverse components, giving a better estimation of anisotropy parameters in the presence of noise and other interfering phases. Future work involves implementing the processing algorithm on real data recorded in São Tomé and Prı́ncipe, Madeira, and Canary islands. This research contributes to the FCT-funded SHAZAM (Ref. PTDC/CTA-GEO/31475/2017) and SIGHT (Ref. PTDC/CTA-GEF/30264/2017) projects.</p>

General Derivation of Frequency-Domain Adaptive Filtering

2001 ◽  
pp. 157-176 ◽  
Author(s):  
Jacob Benesty ◽  
Tomas Gänsler ◽  
Dennis R. Morgan ◽  
M. Mohan Sondhi ◽  
Steven L. Gay
Keyword(s):  
Frequency Domain ◽  
Adaptive Filtering ◽  
General Derivation
Sign in / Sign up

Export Citation Format

Share Document