A real-time atmospheric turbulence mitigation and super-resolution solution for infrared imaging systems

Abstract

Imagery acquired with modern imaging systems is susceptible to a variety of degradations, including blur from the point spread function (PSF) of the imaging system, aliasing from undersampling, blur and warping from atmospheric turbulence, and noise. A variety of image restoration methods have been proposed that estimate an improved image by processing a sequence of these degraded images. In particular, multi-frame image restoration has proven to be a particularly powerful tool for atmospheric turbulence mitigation (TM) and super-resolution (SR). However, these degradations are rarely addressed simultaneously using a common algorithm architecture, and few TM or SR solutions are capable of performing robustly in the presence of true scene motion, such as moving dismounts. Still fewer TM or SR algorithms have found their way into practical real-time implementations. In this paper, we describe a new L-3 joint TM and SR (TMSR) real-time processing solution and demonstrate its capabilities. The system employs a recently developed versatile multi-frame joint TMSR algorithm that has been implemented using a real-time, low-power FPGA processor system. The L-3 TMSR solution can accommodate a wide spectrum of atmospheric conditions and can robustly handle moving vehicles and dismounts. This novel approach unites previous work in TM and SR and also incorporates robust moving object detection. To demonstrate the capabilities of the TMSR solution, results using field test data captured under a variety of turbulence levels, optical configurations, and applications are presented. The performance of the hardware implementation is presented, and we identify specific insertion paths into tactical sensor systems.