A MAP Estimator for Simultaneous Superresolution and Detector Nonunifomity Correction
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Research Article A MAP Estimator for Simultaneous Superresolution and Detector Nonunifomity Correction Russell C. Hardie1 and Douglas R. Droege2 1 Department 2 L-3
of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH 45469-0226, USA Communications Cincinnati Electronics, 7500 Innovation Way, Mason, OH 45040, USA
Received 31 August 2006; Accepted 9 April 2007 Recommended by Richard R. Schultz 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 maximum a 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 a variety of ways including using a moving temporal window of frames to process successive groups of frames. By combining SR and nonuniformity correction (NUC) in this fashion, we demonstrate that superior results are possible compared with the more conventional approach of performing scene-based NUC followed by independent SR. The proposed MAP algorithm can be applied with or without SR, depending on the application and computational resources available. Even without SR, we believe that the proposed algorithm represents a novel and promising scene-based NUC technique. We present a number of experimental results to demonstrate the efficacy of the proposed algorithm. These include simulated imagery for quantitative analysis and real infrared video for qualitative analysis. Copyright © 2007 R. C. Hardie and D. R. Droege. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
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 [1–4]. Nonuniformity, or fixed pattern noise, results from nonuniform responsivity of the photodetectors that make up the FPA. This nonuniformity tends to drift over time, precluding a simple one-time factory correction from completely eradicating the problem. Traditional methods of reducing fixed pattern noise, such as correlated double sampling [5], are often ineffective because the processing technology and operating temperatures of infrared sensor materials result in the dominance of different sources of nonuniformity. Periodic calibration techniques can be employed to address the prob
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