Spatial adaptive regularized MAP reconstruction for LD-based night vision

Target recognition plays a vital role in video surveillance, land and ocean exploration, in which imaging detection especially night vision is widely used. It is known that imagery detection is affected not only by absorption and scattering properties of atmosphere or water, but also the optical systems including lenses and sensors. In order to enhance the visual quality of the night vision detecting images to a best possible level, applications such as restoration and super-resolution reconstruction can be applied. The presented effort applies a spatial adaptive regularized MAP reconstruction method to a night vision laser diode (LD) imaging detection system. Blind, objective image quality metrics are adopted for determining the iteration time. Experimental results of the target recognition show adequate improvement in the detecting range as well as the detecting quality provided by the proposed approaches.