International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
An axiomatic approach to image interpolation
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
Comparison of Graph Cuts with Belief Propagation for Stereo, using Identical MRF Parameters
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Fundamental Limits of Reconstruction-Based Superresolution Algorithms under Local Translation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Colorization using optimization
ACM SIGGRAPH 2004 Papers
Patch Based Blind Image Super Resolution
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Perceptually-Inspired and Edge-Directed Color Image Super-Resolution
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Image upsampling via imposed edge statistics
ACM SIGGRAPH 2007 papers
ACM SIGGRAPH 2007 papers
Face Hallucination: Theory and Practice
International Journal of Computer Vision
ACM SIGGRAPH Asia 2008 papers
ACM SIGGRAPH Asia 2008 papers
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This paper introduces a new procedure to handle color in single image super resolution (SR). Most existing SR techniques focus primarily on enforcing image priors or synthesizing image details; less attention is paid to the final color assignment. As a result, many existing SR techniques exhibit some form of color aberration in the final upsampled image. In this paper, we outline a procedure based on image colorization and back-projection to perform color assignment guided by the super-resolution luminance channel. We have found that our procedure produces better results both quantitatively and qualitatively than existing approaches. In addition, our approach is generic and can be incorporated into any existing SR techniques.