Super-Resolution Imaging Enhancement through a 2D Scanning Galvanometer: Algorithm Formulation and Application in Aerial Optoelectronic Systems

As the fields of aviation and aerospace optics continue to evolve, there is an increasing demand for enhanced detection capabilities in equipment. Nonetheless, in applications where both optical and mechanical constraints are stringent, the continuous expansion of optical aperture and focal length is impractical. Given the existing technological landscape, employing super-resolution algorithms to enhance the imaging capability of optical systems is both practical and highly relevant. This study capitalizes on using a 2D scanning galvanometer in optical systems to acquire micro-displacement information. Initially, an imaging model for optical systems equipped with a 2D scanning galvanometer was established, and the displacement vectors for both forward and sweep image motions were defined. On this foundation, we incorporated micro-displacement information that can induce high-frequency aliasing. Subsequently, the motion paths of the galvanometer were planned and modeled. To align image sequences with micro-displacement correlations, the Lucas-Kanade (L-K) optical flow method was employed with multi-layer pyramid iteration. Then, super-resolution reconstruction was performed using kernel regression techniques. Ultimately, we tested the algorithm on an aeronautical optoelectronic pod to evaluate its impact on optical resolution and imaging quality. Compared with the original images, the 16-frame image demonstrated a 39% improvement in optical resolution under laboratory conditions. Moreover, the algorithm exhibited satisfactory performance under both nighttime and daytime conditions, as well as during aerial tests.