Effective preprocessing stage in the fourier transform domain for image quality assessment

Systems of first-order partial differential equations are considered and the possible decomposition of the solutions in forward and backward propagating is investigated. After a review of a customary procedure in the space-time domain (wave splitting), attention is addressed to systems in the Fourier-transform domain, thus considering frequency-dependent functions of the space variable. The characterization is given for the direction of propagation and applications are developed to some cases of physical interest.

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Seismic trace interpolation in the Fourier transform domain

Geophysics ◽

10.1190/1.1543221 ◽

2003 ◽

Vol 68 (1) ◽

pp. 355-369 ◽

Cited By ~ 109

Author(s):

Necati Gülünay

Keyword(s):

Fourier Transform ◽

Fourier Transforms ◽

Interpolation Method ◽

Original Data ◽

Transform Domain ◽

3D Data ◽

Adaptive Interpolation ◽

Data Adaptive ◽

The Fourier Transform ◽

Fourier Transform Domain

A data adaptive interpolation method is designed and applied in the Fourier transform domain (f‐k or f‐kx‐ky for spatially aliased data. The method makes use of fast Fourier transforms and their cyclic properties, thereby offering a significant cost advantage over other techniques that interpolate aliased data. The algorithm designs and applies interpolation operators in the f‐k (or f‐kx‐ky domain to fill zero traces inserted in the data in the t‐x (or t‐x‐y) domain at locations where interpolated traces are needed. The interpolation operator is designed by manipulating the lower frequency components of the stretched transforms of the original data. This operator is derived assuming that it is the same operator that fills periodically zeroed traces of the original data but at the lower frequencies, and corresponds to the f‐k (or f‐kx‐ky domain version of the well‐known f‐x (or f‐x‐y) domain trace interpolators. The method is applicable to 2D and 3D data recorded sparsely in a horizontal plane. The most common prestack applications of the algorithm are common‐mid‐point domain shot interpolation, source‐receiver domain shot interpolation, and cable interpolation.

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The N-Dimensional Uncertainty Principle for the Free Metaplectic Transformation

Mathematics ◽

10.3390/math8101685 ◽

2020 ◽

Vol 8 (10) ◽

pp. 1685

Author(s):

Rui Jing ◽

Bei Liu ◽

Rui Li ◽

Rui Liu

Keyword(s):

Fourier Transform ◽

Quantum Mechanics ◽

Uncertainty Principle ◽

Transformation Operator ◽

Local Analysis ◽

Transform Domain ◽

Uncertainty Principles ◽

The Fourier Transform ◽

Heisenberg's Uncertainty Principle ◽

Fourier Transform Domain

The free metaplectic transformation is an N-dimensional linear canonical transformation. This transformation operator is useful, especially for signal processing applications. In this paper, in order to characterize simultaneously local analysis of a function (or signal) and its free metaplectic transformation, we extend some different uncertainty principles (UP) from quantum mechanics including Classical Heisenberg’s uncertainty principle, Nazarov’s UP, Donoho and Stark’s UP, Hardy’s UP, Beurling’s UP, Logarithmic UP, and Entropic UP, which have already been well studied in the Fourier transform domain.

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Image Quality Assessment Method Based on Fractional Fourier Transform

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.433-435.372 ◽

2013 ◽

Vol 433-435 ◽

pp. 372-375

Author(s):

Bin Wang

Keyword(s):

Fourier Transform ◽

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Fractional Fourier Transform ◽

Quality Score ◽

Reference Image ◽

Support Vector ◽

Two Dimensional ◽

Assessment Approach

Image quality assessment is an important issue in the area of image processing, and the no-reference image quality assessment tries to evaluate the quality of image without the reference image. The present no-reference image quality assessment approach can not predict the quality score accurately. This paper proposes a new image quality assessment approach based on two-dimensional discrete fractional Fourier transform (FRFT). After the image is processed by two dimensional discrete fourier transform, the histogram of FRFT coefficients in different order are modeled by generalized Gaussian distribution (GGD). The parameters of GGD are estimated and the feature vector is formed by parameters of GGD. After that, the image is classified into five distortion type by the trained support vector machine. At last, the quality score is predicted by the trained support vector regression machine. The experiment results show that the performance of proposed method is better than the traditional method.

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