This paper describes the design of an efficient filter that promises to significantly improve the performance of second-generation Forward Looking Infrared (FLIR) and other digital imaging systems. The filter is based on a comprehensive model of the digital imaging process that accounts for the significant effects of sampling and reconstruction as well as acquisition blur and noise. The filter both restores, partially correcting degradations introduced during image acquisition, and interpolates, increasing apparent resolution and improving reconstruction. The filter derivation is conditioned on explicit constraints on spatial support and resolution so that it can be implemented efficiently and is practical for real-time applications. Subject to these implementation constraints, the filter optimizes end-to-end system fidelity. In experiments with simulated FLIR systems, the filter significantly increases fidelity, apparent resolution, effective range, and visual quality for a range of conditions with relatively little computation.