Image restoration using fast Fourier and wavelet transforms

William J. Harrod; James G. Nagy; Robert J. Plemmons

doi:10.1117/12.172516

1 February 1994 Image restoration using fast Fourier and wavelet transforms

William J. Harrod, James G. Nagy, Robert J. Plemmons

Proceedings Volume 2093, Substance Identification Analytics; (1994) https://doi.org/10.1117/12.172516
Event: Substance Identification Technologies, 1993, Innsbruck, Austria

Abstract

Pure nuclear quadrupole resonance (NQR) of ¹⁴N nuclei is quite promising as a method for detecting explosives such as RDX and contraband narcotics such as cocaine and heroin in quantities of interest. Pure NQR is conducted without an external applied magnetic field, so potential concerns about damage to magnetically encoded data or exposure of personnel to large magnetic fields are not relevant. Because NQR frequencies of different compounds are quite distinct, we do not encounter false alarms from the NQR signals of other benign materials. We have constructed a proof-of-concept NQR explosives detector which interrogates a volume of 300 liters (10 ft³). With minimal modification to the existing explosives detector, we can detect operationally relevant quantities of (free base) cocaine within the 300-liter inspection volume in 6 seconds. We are presently extending this approach to the detection of heroin base and also examining ¹⁴N and ^35,37Cl pure NQR for detection of the hydrochloride forms of both materials. An adaptation of this NQR approach may be suitable for scanning personnel for externally carried contraband and explosives. We first outline the basics of the NQR approach, highlighting strengths and weaknesses, and then present representative results for RDX and cocaine detection. We also present a partial compendium of relevant NQR parameters measured for some materials of interest.

Citation Download Citation

William J. Harrod, James G. Nagy, and Robert J. Plemmons "Image restoration using fast Fourier and wavelet transforms", Proc. SPIE 2093, Substance Identification Analytics, (1 February 1994); https://doi.org/10.1117/12.172516

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