Development Of A High Performance Mosaicing And Super-Resolution Algorithm

In this dissertation, a high-performance mosaicing and super-resolution algorithm is described. The scale invariant feature transform (SIFT)-based mosaicing algorithm builds an initial mosaic which is iteratively updated by the robust super resolution algorithm to achieve the final high-resolution mosaic. Two different types of datasets are used for testing: high altitude balloon data and unmanned aerial vehicle data. To evaluate our algorithm, five performance metrics are employed: mean square error, peak signal to noise ratio, singular value decomposition, slope of reciprocal singular value curve, and cumulative probability of blur detection. Extensive testing shows that the proposed algorithm is effective in improving the captured aerial data and the performance metrics are accurate in quantifying the evaluation of the algorithm

Multi-frame super-resolution from observations with zooming motion

This paper proposes a new multi-frame super-resolution (SR) approach to reconstruct a high-resolution (HR) image from low-resolution (LR) images with zooming motion. Existing SR methods often assume that the relative motion between acquired LR images consists of only translation and possibly rotation. This restricts the application of these methods in cases when there is zooming motion among the LR images. There are currently only a few methods focusing on zooming SR. Their formulation usually ignores the registration errors or assumes them to be negligible during the HR reconstruction. In view of this, this paper presents a new SR reconstruction approach to handle a more flexible motion model including translation, rotation and zooming. An iterative framework is developed to estimate the motion parameters and HR image progressively. Both simulated and real-life experiments show that the proposed method is effective in performing image SR