International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image upsampling via imposed edge statistics
ACM SIGGRAPH 2007 papers
Face Hallucination: Theory and Practice
International Journal of Computer Vision
PatchMatch: a randomized correspondence algorithm for structural image editing
ACM SIGGRAPH 2009 papers
Image super-resolution via sparse representation
IEEE Transactions on Image Processing
SIFT Flow: Dense Correspondence across Scenes and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expression flow for 3D-aware face component transfer
ACM SIGGRAPH 2011 papers
Non-rigid dense correspondence with applications for image enhancement
ACM SIGGRAPH 2011 papers
Image enhancement by nonlinear extrapolation in frequency space
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
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In most image hallucination work, a strong assumption is held that images can be aligned to a template on which the prior of high-res images is formulated and learned. Realizing that one template can hardly generalize to all images of an object such as faces due to pose and viewpoint variation as well as occlusion, we propose an example-based prior distribution via dense image correspondences. We introduce a Bayesian formulation based on an image prior that can implement different effective behaviors based on the value of a single parameter. Using faces as examples, we show that our system outperforms the prior state of art.