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 a variety of ways including using amoving 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.