TY - GEN
T1 - Scene-based adaptive spectral sensing systems based on quantum dots infrared photodetectors
AU - Wang, Zhipeng
AU - Tyo, J. Scott
PY - 2009
Y1 - 2009
N2 - Significant advances have been made in developing normal-incidence sensitive quantum-dot infrared photodetectors (QDIPs) for midwave- and longwave-infrared imaging systems. QDIPs with nanoscale asymmetrical structures of the quantum dots can exhibit spectral responses tunable through the bias voltages applied. This makes it possible to build spectral imaging system in IR range based on single QDIP, without any spectral dispersive device upfront. Further more, unlike conventional systems whose spectral bands are fixed for various tasks which leads to data redundancy, the QDIP based system can be operated as being adaptive to scenes if different sets of operating bias voltages are selected for different tasks. To achieve such adaptivity, optimization algorithms must be developed to find the scene-based operation bias voltages set which maximizes the spectral context inside the output data while reducing the data redundancy. In this paper, we devise a series of optimization methods based on a recently developed geometrical spectral imaging model (Wang et al., 2007):1 In the beginning, an scene-independent set of bias voltages is selected to maximize the average signal-to-noise ratio (SNR) of the sensor. Then, some bias voltages are added or removed based on the captured data. This dynamic optimization process is performed throughout the imaging process so that the balance between data information and data volume is always achieved. Due to the universality of the algorithm, this optimization process can be applied to any spectral sensor whose spectral response functions are known.
AB - Significant advances have been made in developing normal-incidence sensitive quantum-dot infrared photodetectors (QDIPs) for midwave- and longwave-infrared imaging systems. QDIPs with nanoscale asymmetrical structures of the quantum dots can exhibit spectral responses tunable through the bias voltages applied. This makes it possible to build spectral imaging system in IR range based on single QDIP, without any spectral dispersive device upfront. Further more, unlike conventional systems whose spectral bands are fixed for various tasks which leads to data redundancy, the QDIP based system can be operated as being adaptive to scenes if different sets of operating bias voltages are selected for different tasks. To achieve such adaptivity, optimization algorithms must be developed to find the scene-based operation bias voltages set which maximizes the spectral context inside the output data while reducing the data redundancy. In this paper, we devise a series of optimization methods based on a recently developed geometrical spectral imaging model (Wang et al., 2007):1 In the beginning, an scene-independent set of bias voltages is selected to maximize the average signal-to-noise ratio (SNR) of the sensor. Then, some bias voltages are added or removed based on the captured data. This dynamic optimization process is performed throughout the imaging process so that the balance between data information and data volume is always achieved. Due to the universality of the algorithm, this optimization process can be applied to any spectral sensor whose spectral response functions are known.
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U2 - 10.1117/12.825142
DO - 10.1117/12.825142
M3 - Conference contribution
AN - SCOPUS:70350152764
SN - 9780819477477
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Imaging Spectrometry XIV
T2 - Imaging Spectrometry XIV
Y2 - 3 August 2009 through 4 August 2009
ER -