Noisy Image Super-resolution by Artificial Neural Networks (CROSBI ID 488770)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Szu, Harold ; Kopriva, Ivica ;
engleski
Noisy Image Super-resolution by Artificial Neural Networks
Noisy incoherent objects, which are too close to be remotely separated by optically imaging beyond the Rayleigh diffraction limit, might be resolved by employing the Artificial Neural Network (ANN) smart pixel post processing and its mathematical framework, Independent Component Analysis (ICA). It is shown that ICA ANN approach to superresolution based on information maximization principle could be seen as a part of the general approach called space-bandwidth (SW) product adaptation method. Our success is perhaps due to the Blind Source Separation (BSS) Smart-Pixel Detectors (SPD) behind the imaging lens (inverse adaptation), while the Rayleigh diffraction limit remains valid for a single instance of the deterministic imaging systems&#8217 ; realization.
Independent Component Analysis; Superresolution; Blind Source Demixing; Focal Plane Arrays.
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Podaci o prilogu
16-20-x.
2001.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of the SPIE 4391
Szu, Harold ; Buss, James ;
Bellingham (WA): SPIE
Podaci o skupu
SPIE AeroSense Symposium - Wavelet Applicatios VIII
predavanje
16.04.2001-20.04.2001
Orlando (FL), Sjedinjene Američke Države