Electromagnetic Techniques for Nondestructive Testing of Dielectric Materials: Diffraction Tomography

Bramanti, M.; Salerno, Emanuele

doi:10.1080/08327823.1992.11688196

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…If the body under test scatters so weakly that the total internal field can be considered equal to the incident field (first-order Born approximation), and if the requirements for considering the field as a scalar quantity are all satisfied, then the complex measured quantity, as a function of frequency (or, equivalently, wavenumber ) and angle , is proportional to (3) where is the two-dimensional (2-D) Fourier transform of the following function: (4) Function is the projection of onto the -plane, orthogonal to the rotation axis of the body. Details on the derivation of (3) can be found in [2], [5], and [7]. We consider function as our object function.…”

Section: A Relationship Between Measured Data and Objectmentioning

confidence: 99%

Microwave tomography of lossy objects from monostatic measurements

Salerno

1999

IEEE Trans. Microwave Theory Techn.

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This paper addresses the problem of microwave tomographic imaging from far-field monostatic measurements. In the first-order Born approximation, a simple Fourier relationship exists between the measured data and dielectric contrast of the object under test. For a lossy object, the contrast function depends on frequency, and this prevents monostatic data from being used directly since multifrequency probing radiation must be used to get sufficient information. To overcome this difficulty, we propose a data preprocessing strategy that eliminates frequency from the function to be reconstructed. The Fourier data obtained through preprocessing could be immediately inverted to give a bandpass estimate of the unknown function. Despite the drawbacks inherent in this approach, in small-scale tomographic applications, the space region occupied by the object under test, as well as the sign of the unknown function, are normally known. This extra information allows us to apply a projected Landweber algorithm to uniquely reconstruct the unknown, thus avoiding the above-mentioned drawbacks. We outline the preprocessing and reconstruction techniques adopted, and show some preliminary results from experimental and simulated data.

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Section: A Relationship Between Measured Data and Objectmentioning

confidence: 99%

Microwave tomography of lossy objects from monostatic measurements

Salerno

1999

IEEE Trans. Microwave Theory Techn.

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Superresolution capabilities of the Gerchberg method in the band‐pass case: An eigenvalue analysis

Salerno

1998

Int J Imaging Syst Tech

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This article first provides a general introduction to the Gerchberg superresolution algorithm. Some specific properties of this algorithm, when applied to 2D band‐pass images, are then studied by means of an eigenvalue analysis of the imaging operator. The main feature derived is the capability to recover the dc component of the unknown object that has to be reconstructed from the noisy image available. This aspect is important with band‐pass images of strictly positive objects, in that recovering the low‐frequency and dc components in this case is tantamount to suppressing intolerable artifacts. A set of eigenpairs of the imaging operator was calculated numerically. From the dominant eigenvalues, the spectrum extrapolation capabilities of the method can be derived. From the behavior of the eigenfunctions, the capability of the method to recover the dc component of the original object can be evaluated. Some of the calculated eigenfunctions are shown as examples. © 1998 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 9, 181–188, 1998

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Electromagnetic Techniques for Nondestructive Testing of Dielectric Materials: Diffraction Tomography

Cited by 2 publications

References 4 publications

Microwave tomography of lossy objects from monostatic measurements

Microwave tomography of lossy objects from monostatic measurements

Superresolution capabilities of the Gerchberg method in the band‐pass case: An eigenvalue analysis

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