Selective Image Super-Resolution
@article{Sun2010SelectiveIS, title={Selective Image Super-Resolution}, author={Ju Sun and Qiang Chen and Shuicheng Yan and Loong Fah Cheong}, journal={ArXiv}, year={2010}, volume={abs/1010.5610} }
In this paper we propose a vision system that performs image Super Resolution (SR) with selectivity. Conventional SR techniques, either by multi-image fusion or example-based construction, have failed to capitalize on the intrinsic structural and semantic context in the image, and performed \blind" resolution recovery to the entire image area. By comparison, we advocate examplebased selective SR whereby selectivity is exemplied in three aspects: region selectivity (SR only at object regions…Â
6 Citations
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This paper proposes the use of Super-Resolution Convolutional Neural Networks (SRCNN) which are constructed to tackle issues associated with characters and text and demonstrates that standard SRCNNs trained for general object super-resolution is not sufficient and that the proposed method is a viable method in creating a robust model for text.
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Experimental results demonstrate that the proposed new super-resolution (SR) scheme achieves significant improvement compared with four state-of-the-art schemes in terms of both subjective and objective qualities.
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A group-structured sparse representation approach to make full use of both internal and external dependencies to facilitate image super-resolution and provides the desired over-completeness property when sparsely coding a given LR patch.
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An advanced Neighbor Embedding based method for Super resolution used in which combine learning technique used to train two projection matrices simultaneously and to map the original Low Resolution and High Resolution feature spaces onto a unified feature subspace.
Advance Neighbor Embedding for Image Super Resolution
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The Advance Neighbor embedding (ANE) method for image super resolution gives better resolution than NE method using combine learning technique used to train two projection matrices simultaneously and to map the original Low Resolution and High Resolution feature spaces onto a unified feature subspace.
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