Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Is Super-Resolution with Optical Flow Feasible?
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Adaptive outlier rejection in image super-resolution
EURASIP Journal on Applied Signal Processing
HOS-based image super-resolution reconstruction
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
An image super-resolution algorithm for different error levels per frame
IEEE Transactions on Image Processing
General choice of the regularization functional in regularized image restoration
IEEE Transactions on Image Processing
Robust methods for high-quality stills from interlaced video in the presence of dominant motion
IEEE Transactions on Circuits and Systems for Video Technology
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As digital video cameras spread rapidly, critical locations such as airports, train stations, military compounds and airbases and public ''hot-spots'' are placed in the spotlight of video surveillance. Video surveillance provides visual information, in order to maintain security at the monitored areas. Indeed, super-resolution (SR) technique can be useful for extracting additional information from the captured video sequences. Generally, video surveillance cameras capture moving objects while cameras are moving in some cases, which means that many of the existing SR algorithms, that cannot cope with moving objects, are not applicable in this case. In this paper, we propose a SR algorithm that takes into account inaccurate registration at the moving regions and therefore copes with moving objects. We propose to adaptively weight each region according to its reliability where regions that have local motion and/or occlusion have different registration error level. Also, the regularization parameter is simultaneously estimated for each region. The regions are generated by segmenting the reference frame using watershed segmentation. Our approach is tested on simulated and real data coming from videos with different difficulties taken by a hand-held camera. The experimental results show the effectiveness of the proposed algorithm compared to four state-of-the-art SR algorithms.