An Efficient and Robust Algorithm for Improving the Resolution of Video Sequences
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Proceedings of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence
A multi-frame image super-resolution method
Signal Processing
Iterated conditional modes for inverse dithering
Signal Processing
A super-resolution reconstruction algorithm for surveillance images
Signal Processing
Generalizing the Nonlocal-means to super-resolution reconstruction
IEEE Transactions on Image Processing
Super resolutionwith probabilistic motion estimation
IEEE Transactions on Image Processing
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Interactive segmentation for manipulation in unstructured environments
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Maximum a posteriori video super-resolution using a new multichannel image prior
IEEE Transactions on Image Processing
New learning based super-resolution: use of DWT and IGMRF prior
IEEE Transactions on Image Processing
New learning based super-resolution: use of DWT and IGMRF prior
IEEE Transactions on Image Processing
Enhanced Local Subspace Affinity for feature-based motion segmentation
Pattern Recognition
Zoom based super-resolution: a fast approach using particle swarm optimization
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
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ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
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IEEE Transactions on Image Processing
Matte super-resolution for compositing
Proceedings of the 32nd DAGM conference on Pattern recognition
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Robotics and Autonomous Systems
A super-resolution reconstruction algorithm for hyperspectral images
Signal Processing
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Super resolution image reconstruction allows the recovery of a high-resolution (HR) image from several low-resolution images that are noisy, blurred, and down sampled. In this paper, we present a joint formulation for a complex super-resolution problem in which the scenes contain multiple independently moving objects. This formulation is built upon the maximum a posteriori (MAP) framework, which judiciously combines motion estimation, segmentation, and super resolution together. A cyclic coordinate descent optimization procedure is used to solve the MAP formulation, in which the motion fields, segmentation fields, and HR images are found in an alternate manner given the two others, respectively. Specifically, the gradient-based methods are employed to solve the HR image and motion fields, and an iterated conditional mode optimization method to obtain the segmentation fields. The proposed algorithm has been tested using a synthetic image sequence, the "Mobile and Calendar" sequence, and the original "Motorcycle and Car" sequence. The experiment results and error analyses verify the efficacy of this algorithm