Hallucinating face by position-patch

By Xiang-Wang Ma, Junping Zhang, Chun Qi. Pattern Recognition, 2010

327 citations recorded by the source corpus.

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Global-Local Face Upsampling Network (2016, background): “[11] assumed that training and test images are precisely aligned and searched the NN patches of a target pixel in the test image only at the specific pixel location in the training images.”
A Comprehensive Survey to Face Hallucination (2013, methodology): “The authors also applied the position-based FH method to multi-view FH in which a multi-view face synthesis procedure was conducted before hallucination by utilizing a method similar to neighbor embedding (Ma et al. 2010a).”
Ultra-Resolving Face Images by Discriminative Generative Networks (2016, methodology): “Instead of imposing global constraints, [8] uses multiple local constraints learned from exemplar patches, and [32] reserves to sparse representation on the local structures of faces.”
Super-resolution: a comprehensive survey (2014, unknown): “Facial Images [57], [71], [82], [85], [99], [100], [105], [127], [142], [154], [165], [187], [188], [189], [190], [191], [192], [193], [194], [200], [208], [235], [270], [276], [285], [298], [299], [301], [302], [304], [310], [311], [313], [314], [322], [323], [325], [327], [338], [339], [340], [341”
Very low resolution face recognition problem (2010, methodology): “Newwdataa constraint Discriminativee constraint Relationship OperatorR VLRRtrainingimages HRRtrainingimages R TestingVLRRimage ReconstructedHRimage”