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- W2085573253 abstract "Image enhancement of low-resolution images can be done through methods such as interpolation, super-resolution using multiple video frames, and example-based super-resolution. Example-based super-resolution, in particular, is suited to images that have a strong prior (for those frameworks that work on only a single image, it is more like image restoration than traditional, multiframe super-resolution). For example, hallucination and Markov random field (MRF) methods use examples drawn from the same domain as the image being enhanced to determine what the missing high-frequency information is likely to be. We propose to use even stronger prior information by extending MRF-based super-resolution to use adaptive observation and transition functions, that is, to make these functions region-dependent. We show with face images how we can adapt the modeling for each image patch so as to improve the resolution." @default.
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- W2085573253 date "2006-02-07" @default.
- W2085573253 modified "2023-10-16" @default.
- W2085573253 title "Adaptive Markov Random Fields for Example-Based Super-resolution of Faces" @default.
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- W2085573253 doi "https://doi.org/10.1155/asp/2006/31062" @default.
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