A unified learning framework for single image super-resolution

IEEE Transactions on Neural Networks and Learning Systems

Volumn 25, Issue 4, 2014, Pages 780-792

  Yu, Jifei ^a   Gao, Xinbo ^a   Tao, Dacheng ^b   Li, Xuelong ^c   Zhang, Kaibing ^d  

^a XIDIAN UNIVERSITY   (China)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^c XI AN INSTITUTE OF OPTICS AND PRECISION MECHANICS   (China)

^d HUBEI ENGINEERING UNIVERSITY   (China)

Author keywords

Example learning based; image super resolution (SR); reconstruction based; Self similarity

Indexed keywords

OPTICAL RESOLVING POWER;

EXAMPLE LEARNING-BASED; HIGH RESOLUTION IMAGE; IMAGE SUPER-RESOLUTION; INTEGRATED FRAMEWORKS; LEARNING-BASED METHODS; NON- LOCAL MEANS FILTERS; RECONSTRUCTION-BASED; SELF-SIMILARITIES;

IMAGE RECONSTRUCTION;

ALGORITHM; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; IMAGE ENHANCEMENT; PHOTOGRAPHY; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; PHOTOGRAPHY; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84897025208     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2013.2281313     Document Type: Article

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