High-Resolution Image Reconstruction from a Sequence of Rotated and Translated Frames and its Application to an Infrared Imaging System

Some imaging systems employ detector arrays which are not su‐ciently dense so as to meet the Nyquist criteria during image acquisition. This is particularly true for many staring infrared imagers. Thus, the full resolution afiorded by the optics is not being realized in such a system. This paper presents a technique for estimating a high resolution image, with reduced aliasing, from a sequence of undersampled rotated and translationally shifted frames. Such an image sequence can be obtained if an imager is mounted on a moving platform, such as an aircraft. Several approaches to this type of problem have been proposed in the literature. Here we extend some of this previous work. In particular, we deflne an observation model which incorporates knowledge of the optical system and detector array. The high resolution image estimate is formed by minimizing a new regularized cost function which is based on the observation model. We show that with the proper choice of a tuning parameter, our algorithm exhibits robustness in the presence of noise. We consider both gradient descent and conjugate gradient optimization procedures to minimize the cost function. Detailed experimental results are provided to illustrate the performance of the proposed algorithm using digital video from an infrared imager.