On bayesian adaptive video super resolution

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 36, Issue 2, 2014, Pages 346-360

  Liu, Ce ^a   Sun, Deqing ^b  

^a MICROSOFT RESEARCH   (United States)

^b HARVARD UNIVERSITY   (United States)

Author keywords

aliasing; blur kernel; noise level; optical flow; Super resolution

Indexed keywords

ALIASING; BAYESIAN APPROACHES; BLUR KERNEL; EXISTING SYSTEMS; INTRINSIC CHARACTERISTICS; NOISE LEVELS; SUPER RESOLUTION; VIDEO SUPER-RESOLUTION;

BAYESIAN NETWORKS; CRAMER-RAO BOUNDS; IMAGE RECONSTRUCTION; OPTICAL FLOWS;

MOTION ESTIMATION;

ALGORITHM; ARTICLE; ARTIFACT; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; COMPUTER ASSISTED DIAGNOSIS; IMAGE ENHANCEMENT; METHODOLOGY; PHOTOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; VIDEORECORDING;

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

EID: 84891593494     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2013.127     Document Type: Article

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