Confidence aware Levenberg Marquardt optimization for joint motion estimation and super resolution
Motion estimation across low resolution frames and the reconstruction of high resolution images are two coupled subproblems of multi frame super resolution. This paper introduces a new joint optimization approach for motion estimation and image reconstruction to address this interdependence. Our method is formulated via non linear least squares optimization and combines two principles of robust super resolution. First, to enhance the robustness of the joint estimation, we propose a confidence aware energy minimization framework augmented with sparse regularization. Second, we develop a tailor made Levenberg Marquardt iteration scheme to jointly estimate motion parameters and the high resolution image along with the corresponding model confidence parameters. Our experiments on simulated and real images confirm that the proposed approach outperforms decoupled motion estimation and image reconstruction as well as related state of the art joint estimation algorithms.
2016 IEEE International Conference on Image Processing (ICIP)