  Wide-field and high-resolution (HR) imaging is essential for various
applications such as aviation reconnaissance, topographic mapping and safety
monitoring. The existing techniques require a large-scale detector array to
capture HR images of the whole field, resulting in high complexity and heavy
cost. In this work, we report an agile wide-field imaging framework with
selective high resolution that requires only two detectors. It builds on the
statistical sparsity prior of natural scenes that the important targets locate
only at small regions of interests (ROI), instead of the whole field. Under
this assumption, we use a short-focal camera to image wide field with a certain
low resolution, and use a long-focal camera to acquire the HR images of ROI. To
automatically locate ROI in the wide field in real time, we propose an
efficient deep-learning based multiscale registration method that is robust and
blind to the large setting differences (focal, white balance, etc) between the
two cameras. Using the registered location, the long-focal camera mounted on a
gimbal enables real-time tracking of the ROI for continuous HR imaging. We
demonstrated the novel imaging framework by building a proof-of-concept setup
with only 1181 gram weight, and assembled it on an unmanned aerial vehicle for
air-to-ground monitoring. Experiments show that the setup maintains
120$^{\circ}$ wide field-of-view (FOV) with selective 0.45$mrad$ instantaneous
FOV.
