Choice of low resolution sample sets for efficient super-resolution signal reconstruction

In applications such as super-resolution imaging and mosaicking, multiple video sequences are registered to reconstruct video with enhanced resolution. However, not all computed registration is reliable. In addition, not all sequences contribute useful information towards reconstruction from multiple non-uniformly distributed sample sets. In this paper we present two algorithms that can help determine which low resolution sample sets should be combined in order to maximize reconstruction accuracy while minimizing the number of sample sets. The first algorithm computes a confidence measure which is derived as a combination of two objective functions. The second algorithm is an iterative ranked-based method for reconstruction which uses confidence measures to assign priority to sample sets that maximize information gain while minimizing reconstruction error. Experimental results with real and synthetic sequences validate the effectiveness of the proposed algorithms. Application of our work in medical visualization and super-resolution reconstruction of MRI data are also presented.

► It synchronizes videos of similar events acquired using uncalibrated cameras.
► It can handle scenes from different views, and videos having dynamic time shift.
► It considers symmetric alignments of videos and computes the optimal alignment.
► It achieves superior performance over existing techniques.
► It has been successfully used in high-resolution 4D MRI data generation.