26 March 2001 Noisy image superresolution by artificial neural networks
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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' realization. The blindness is due to the unknown objects, and the unpredictable propagation effect on the net imaging point spread function. Such a software/firmware enhancement of imaging system may have a profound implication to the designs of the new (third) generation imaging systems as well as other non-optical imaging systems.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Harold H. Szu, Harold H. Szu, Ivica Kopriva, Ivica Kopriva, } "Noisy image superresolution by artificial neural networks", Proc. SPIE 4391, Wavelet Applications VIII, (26 March 2001); doi: 10.1117/12.421186; https://doi.org/10.1117/12.421186

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