Performance Evaluations for Super-Resolution Mosaicing on UAS Surveillance Videos. (17th May 2013)

• Record Type: Journal Article
• Authors: Camargo, Aldo; He, Qiang; Palaniappan, Kannappan
• Is Part Of: International journal of advanced robotic systems. Volume 10:Number 5(2013)
• Publication Date: 2013-05-17
• Subjects: Super-Resolution -- Conjugate Gradient Method -- Steepest Descent Method -- Levenberg-Marquardt Algorithm -- Ill-Conditioned Problems -- Video Mosaicing; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.5772/56534
• Languages: English
• ISSNs: 1729-8806

Abstract

Abstract 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.