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30 October 2009 Research on super-resolution based on random fields for low-level vision
Min Li, Xiaofeng Liu
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Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74962E (2009) https://doi.org/10.1117/12.833735
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
Abstract
The goal of single-frame Super-Resolution is to improve the spatial resolution of a given low-resolution image. However, it is ill-posed. Regularization which can be interpreted as the way of finding the prior distribution of images plays a crucial role in solving this problem. Example-based approach is one of the well-established regularization techniques for image process based on the prior information stored in the database, which is also used for image Super-Resolution reconstruction. This paper previews the Exampled-based Super-Resolution approach which is based on Freeman's work. We show how the example images to be used to generate training set, describing the Super-Resolution synthesis processing based on the training set, with the plausible experiment results on the single-frame image scale-up. Finally, the related problems and future challenges in this field are also mentioned.
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Min Li and Xiaofeng Liu "Research on super-resolution based on random fields for low-level vision", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74962E (30 October 2009); https://doi.org/10.1117/12.833735
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KEYWORDS
Super resolution
Image processing
Evolutionary algorithms
Reconstruction algorithms
Databases
Image filtering
Image quality
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