Agile wide-field imaging with selective high resolution

Wide-field and high-resolution (HR) imaging are 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 interest (ROI), instead of the whole field. Under this assumption, we use a short-focal camera to image a 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 degrees wide field of view (FOV) with selective 0.45mrad instantaneous FOV. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Automated summary

This study presents an agile wide-field imaging framework with selective high resolution, requiring only two detectors. By leveraging the statistical sparsity prior of natural scenes, it uses a short-focal camera for low-resolution imaging and a long-focal camera for high-resolution imaging of regions of interest (ROI), enabling real-time tracking of ROI.