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Impact Of Detector-Element Active-Area Shape And Fill Factor On Image Sampling, Restoration, And Super-Resolution, Russell C. Hardie, Douglas R. Droege, Alexander J. Dapore, Mark E. Greiner
Impact Of Detector-Element Active-Area Shape And Fill Factor On Image Sampling, Restoration, And Super-Resolution, Russell C. Hardie, Douglas R. Droege, Alexander J. Dapore, Mark E. Greiner
Russell C. Hardie
In many undersampled imaging systems, spatial integration from the individual detector elements is the dominant component of the system point spread function (PSF). Conventional focal plane arrays (FPAs) utilize square detector elements with a nearly 100% fill factor, where fill factor is defined as the fraction of the detector element area that is active in light detection. A large fill factor is generally considered to be desirable because more photons are collected for a given pitch, and this leads to a higher signal-to-noise-ratio (SNR). However, the large active area works against super-resolution (SR) image restoration by acting as an additional …
A Map Estimator For Simultaneous Superresolution And Detector Nonunifomity Correct, Russell Hardie, Douglas Droege
A Map Estimator For Simultaneous Superresolution And Detector Nonunifomity Correct, Russell Hardie, Douglas Droege
Russell C. Hardie
During digital video acquisition, imagery may be degraded by a number of phenomena including undersampling, blur, and noise. Many systems, particularly those containing infrared focal plane array (FPA) sensors, are also subject to detector nonuniformity. Nonuniformity, or fixed pattern noise, results from nonuniform responsivity of the photodetectors that make up the FPA. Here we propose a maximuma posteriori (MAP) estimation framework for simultaneously addressing undersampling, linear blur, additive noise, and bias nonuniformity. In particular, we jointly estimate a superresolution (SR) image and detector bias nonuniformity parameters from a sequence of observed frames. This algorithm can be applied to video in …