Directional Image Filtering Based on the Fourier Transform

2014 ◽  
Vol 19 (2-3) ◽  
pp. 7-13
Author(s):  
Przemysław Korohoda ◽  
Joanna Grabska-Chrząstowska ◽  
Jaromir Przybyło
Keyword(s):  
Fourier Transform ◽  
Rotation Angle ◽  
Image Filtering ◽  
Suggested Method ◽  
Fourier Domain ◽  
Fourier Space ◽  
Direct Rotation ◽  
The Fourier Transform ◽  
Directional Image

Abstract An algorithm to design the small size 2-D filter masks with arbitrarily selected rotation angle has been proposed. The classical filter mask of size 3 × 3 is obtained from the reference Fourier space characteristics, rotated in the Fourier domain. The efficiency of the suggested method was illustrated with examples based on the Sobel gradient mask and two test images. Comparative computations indicated that the accuracy of the filtering result with use of the small size filters is noticeably better when the filter has been designed with use of the Fourier characteristics rotation than after direct rotation of the mask in the pixel domain.

scholarly journals A Sharp Rearrangement Principle in Fourier Space and Symmetry Results for PDEs with Arbitrary Order

2020 ◽  
Author(s):  
Enno Lenzmann ◽  
Jérémy Sok
Keyword(s):  
Fourier Transform ◽  
Phase Retrieval ◽  
Differential Operators ◽  
Arbitrary Order ◽  
Radial Symmetry ◽  
Fourier Space ◽  
Polyharmonic Operator ◽  
Pseudo Differential Operators ◽  
Retrieval Problem ◽  
The Fourier Transform

Abstract We prove sharp inequalities for the symmetric-decreasing rearrangement in Fourier space of functions in $\mathbb{R}^d$. Our main result can be applied to a general class of (pseudo-)differential operators in $\mathbb{R}^d$ of arbitrary order with radial Fourier multipliers. For example, we can take any positive power of the Laplacian $(-\Delta )^s$ with $s> 0$ and, in particular, any polyharmonic operator $(-\Delta )^m$ with integer $m \geqslant 1$. As applications, we prove radial symmetry and real-valuedness (up to trivial symmetries) of optimizers for (1) Gagliardo–Nirenberg inequalities with derivatives of arbitrary order, (2) ground states for bi- and polyharmonic nonlinear Schrödinger equations (NLS), and (3) Adams–Moser–Trudinger type inequalities for $H^{d/2}(\mathbb{R}^d)$ in any dimension $d \geqslant 1$. As a technical key result, we solve a phase retrieval problem for the Fourier transform in $\mathbb{R}^d$. To achieve this, we classify the case of equality in the corresponding Hardy–Littlewood majorant problem for the Fourier transform in $\mathbb{R}^d$.

Parameters Estimation and Waveform Reconstruction of Chirp Signal Based on FRFT

2014 ◽  
Vol 989-994 ◽  
pp. 3993-3996 ◽  
Author(s):  
Yan Jun Wu ◽  
Gang Fu ◽  
Fei Liu
Keyword(s):  
Fourier Transform ◽  
Fractional Fourier Transform ◽  
Signal Reconstruction ◽  
Parameters Estimation ◽  
The Other ◽  
Fourier Domain ◽  
Chirp Signal ◽  
The Fourier Transform ◽  
Definition Of ◽  
Noisy Background

The fractional Fourier transform (FRFT) is a generalization of the Fourier transform. The article first introduces the definition of FRFT transformation; then analyzed FRFT Chirp signal based on this humble proposed restoration Chirp signal in a noisy background in two ways: one is based on parameter estimation, and the other is based on the scores Fourier domain filtering to achieve signal reconstruction; Finally, simulation verify the feasibility of the above analysis.

scholarly journals Dynamic RCS Estimation according to Drone Movement Using the MoM and Far-Field Approximation

2021 ◽  
Vol 21 (4) ◽  
pp. 322-328
Author(s):  
Dong-Yeob Lee ◽  
Jae-In Lee ◽  
Dong-Wook Seo
Keyword(s):  
Fourier Transform ◽  
Cross Section ◽  
Method Of Moments ◽  
Iterative Process ◽  
Rotation Angle ◽  
Field Approximation ◽  
Far Field ◽  
Impedance Matrix ◽  
The Matrix ◽  
The Fourier Transform

Micro-Doppler signatures from the rotating propellers of a drone can be utilized to distinguish the drone from clutter or airborne organisms with similar radar cross section (RCS) levels, such as birds and bats. To obtain the micro-Doppler signatures of a drone, calculation or measurement of the electric field scattered from the rotating propellers is essential. In this paper, using the relative angle concept and far-field approximation, we propose a way to rapidly estimate the dynamic RCS of a drone with several propellers according to its movement. In addition, based on the fact that the shape of the propeller does not change even if it rotates, we construct an impedance matrix only once and apply the matrix to the method of moments instead of the iterative process of calculating the impedance matrix and inverse matrix for each rotation angle of the propeller. Finally, by using the Fourier transform of the results from the proposed method, the rotation frequencies of the propellers according to the movement of the drone can be obtained.

Temporal windowing and inverse transform of the wavefield in the Laplace-Fourier domain

Geophysics ◽  
2013 ◽  
Vol 78 (5) ◽  
pp. R207-R222 ◽  
Author(s):  
Sangmin Kwak ◽  
Hyunggu Jun ◽  
Wansoo Ha ◽  
Changsoo Shin
Keyword(s):  
Fourier Transform ◽  
Time Window ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Fourier Domain ◽  
Gain Function ◽  
The Fourier Transform ◽  
The Time Domain ◽  
Derivatives Of ◽  
Complex Damping

Temporal windowing is a valuable process, which can help us to focus on a specific event in a seismogram. However, applying the time window is difficult outside the time domain. We suggest a windowing method which is applicable in the Laplace-Fourier domain. The window function we adopt is defined as a product of a gain function and an exponential damping function. The Fourier transform of a seismogram windowed by this function is equivalent to the partial derivative of the Laplace-Fourier domain wavefield with respect to the complex damping constant. Therefore, we can obtain a windowed seismogram using the partial derivatives of the Laplace-Fourier domain wavefield. We exploit the time-windowed wavefield, which is modeled directly in the Laplace-Fourier domain, to reconstruct subsurface velocity model by waveform inversion in the Laplace-Fourier domain. We present the windowed seismograms by introducing an inverse Laplace-Fourier transform technique and demonstrate the effect of temporal windowing in a synthetic Laplace-Fourier domain waveform inversion example.

On the Simulation of EEL Spectra Corresponding to Solid and Gaseous Samples

1990 ◽  
Vol 48 (2) ◽  
pp. 18-19
Author(s):  
G. Zanchi ◽  
Y. Kihn ◽  
J. Sévely
Keyword(s):  
Fourier Transform ◽  
Transfer Function ◽  
Energy Loss ◽  
Electron Interaction ◽  
Fourier Space ◽  
Angular Scattering ◽  
The Fourier Transform ◽  
Image Position ◽  
Electron Energy Loss ◽  
Electron Energy Loss Spectra

The electron energy loss spectra can be considered as the result of the convolution of elementary inelastic scattering processes (1, 2). We have developed a procedure which allows to write the intensity of the spectrum as a function of the energy loss.These calculations take the electron angular scattering into account.The probability for an electron to suffer an energy loss E and to be deviated through an angle after a single electron-electron interaction of any kind is given by a normalized function D(E, ), which can be written with a good approximation as a product of two functions g(E) and f(), separately normalized. Assuming that the excitation probabilities of any interaction follows a Poisson distribution, for a collection angle θd the intensity of the spectrum can be written in the Fourier space :Gs(ω) is the Fourier transform of GS(E) which characterizes the transfer function of the experimental device.

Fractional Fourier Transform Image Analysis

2012 ◽  
Vol 198-199 ◽  
pp. 288-293
Author(s):  
Hui Yan Xu
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Fractional Fourier Transform ◽  
Point Of View ◽  
Fourier Domain ◽  
Phase Reconstruction ◽  
Time Frequency ◽  
Space Frequency ◽  
The Fourier Transform ◽  
Simulation Point

As a generalized form of the Fourier transform, fractional Fourier transform (FRFT) ,which is integrated the signal in time domain and frequency domain, is a new time-frequency analysis. From the simulation point of view to image the distribution of energy in the fractional Fourier domain, the amplitude and phase characteristics, simulation results show that any fractional Fourier domain, can reflect the image of the space-frequency domain characteristics, with the order, the image The distribution of the space-frequency domain characteristics will change. The image of the fractional Fourier transform amplitude and phase reconstruction of the image information with the original image also showed some important conclusions for the fractional Fourier transform applied to image recognition and edge detection is of great significance.

The extended Fourier algorithm: Application in discrete optics and electron holography

1994 ◽  
Vol 52 ◽  
pp. 918-919
Author(s):  
E. Voelkl ◽  
L. F. Allard
Keyword(s):  
Fourier Transform ◽  
Fourier Transforms ◽  
Sampling Rate ◽  
Electron Holography ◽  
Optical Bench ◽  
Fourier Space ◽  
Ccd Cameras ◽  
Simulated Image ◽  
Initial Sampling ◽  
Fourier Algorithm

The conventional discrete Fourier transform can be extended to a discrete Extended Fourier transform (EFT). The EFT allows to work with discrete data in close analogy to the optical bench, where continuous data are processed. The EFT includes a capability to increase or decrease the resolution in Fourier space (thus the argument that CCD cameras with a higher number of pixels to increase the resolution in Fourier space is no longer valid). Fourier transforms may also be shifted with arbitrary increments, which is important in electron holography. Still, the analogy between the optical bench and discrete optics on a computer is limited by the Nyquist limit. In this abstract we discuss the capability with the EFT to change the initial sampling rate si of a recorded or simulated image to any other(final) sampling rate sf.

Efficient variations of the Fourier transform in applications to option pricing

2014 ◽  
Vol 18 (2) ◽  
pp. 57-90 ◽  
Author(s):  
Svetlana Boyarchenko ◽  
Sergei Levendorski˘ı
Keyword(s):  
Fourier Transform ◽  
Option Pricing ◽  
The Fourier Transform

scholarly journals Strain Sensitivity Enhancement of Broadband Ultrasonic Signals in Plates Using Spectral Phase Filtering

2021 ◽  
Vol 11 (6) ◽  
pp. 2582
Author(s):  
Lucas M. Martinho ◽  
Alan C. Kubrusly ◽  
Nicolás Pérez ◽  
Jean Pierre von der Weid
Keyword(s):  
Fourier Transform ◽  
Guided Waves ◽  
Strain Level ◽  
Energy Concentration ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Frequency Representation ◽  
The Fourier Transform ◽  
Short Time ◽  
Sensitivity Gain

The focused signal obtained by the time-reversal or the cross-correlation techniques of ultrasonic guided waves in plates changes when the medium is subject to strain, which can be used to monitor the medium strain level. In this paper, the sensitivity to strain of cross-correlated signals is enhanced by a post-processing filtering procedure aiming to preserve only strain-sensitive spectrum components. Two different strategies were adopted, based on the phase of either the Fourier transform or the short-time Fourier transform. Both use prior knowledge of the system impulse response at some strain level. The technique was evaluated in an aluminum plate, effectively providing up to twice higher sensitivity to strain. The sensitivity increase depends on a phase threshold parameter used in the filtering process. Its performance was assessed based on the sensitivity gain, the loss of energy concentration capability, and the value of the foreknown strain. Signals synthesized with the time–frequency representation, through the short-time Fourier transform, provided a better tradeoff between sensitivity gain and loss of energy concentration.

Determination of starch crystallinity with the Fourier-transform terahertz spectrometer

2021 ◽  
Vol 262 ◽  
pp. 117928
Author(s):  
Shusaku Nakajima ◽  
Shuhei Horiuchi ◽  
Akifumi Ikehata ◽  
Yuichi Ogawa
Keyword(s):  
Fourier Transform ◽  
The Fourier Transform ◽  
Starch Crystallinity
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