Super-Resolution for Facial Images Based on Local Similarity Preserving

Jin-Ping HE  Guang-Da SU  Jian-Sheng CHEN  

IEICE TRANSACTIONS on Information and Systems   Vol.E95-D   No.3   pp.892-896
Publication Date: 2012/03/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E95.D.892
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Image Processing and Video Processing
super-resolution for facial images,  manifold learning,  local similarity preserving,  point-based entropy,  energy-coherent compensation,  

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To reconstruct low-resolution facial photographs which are in focus and without motion blur, a novel algorithm based on local similarity preserving is proposed. It is based on the theories of local manifold learning. The innovations of the new method include mixing point-based entropy and Euclidian distance to search for the nearest points, adding point-to-patch degradation model to restrict the linear weights and compensating the fusing patch to keep energy coherence. The compensation reduces the algorithm dependence on training sets and keeps the luminance of reconstruction constant. Experiments show that our method can effectively reconstruct 1612 images with the magnification of 88 and the 3224 facial photographs in focus and without motion blur.