Performance Evaluations for Super-Resolution Mosaicing on UAS Surveillance Videos

Unmanned Aircraft Systems (UAS) have been widely applied for reconnaissance and surveillance by exploiting information collected from the digital imaging payload. The super‐resolution (SR) mosaicing of low‐resolution (LR) UAS surveillance video frames has become a critical requirement for UAS video processing and is important for further effective image understanding. In this paper we develop a novel super‐resolution framework, which does not require the construction of sparse matrices. The proposed method implements image operations in the spatial domain and applies an iterated back‐projection to construct super‐resolution mosaics from the overlapping UAS surveillance video frames. The Steepest Descent method, the Conjugate Gradient method and the Levenberg‐Marquardt algorithm are used to numerically solve the nonlinear optimization problem for estimating a super‐resolution mosaic. A quantitative performance comparison in terms of computation time and visual quality of the super‐ resolution mosaics through the three numerical techniques is presented