High-resolution image reconstruction from a sequence of rotated and translated infrared images

Some imaging systems employ detector arrays which are not sufficiently dense so as to meet the Nyquist criteria during image acquisition. This is particularly true for many staring infrared images. Thus, the full resolution afforded 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 define an observation model which incorporates knowledge of the optical system and detector array. The high resolution image estimate is formed by minimizing a regularized cost function which is based on the observation model. We consider both gradient descent and conjugate gradient optimization procedures to minimize the cost function. We show that the conjugate gradient optimization provides rapid convergence. Detailed experimental results are provided to illustrate the performance of the proposed algorithm using both visible and infrared images. Quantitative error analysis is provided and several images are shown for subjective evaluation.