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SYSTEMS AND METHODS FOR SUPER-RESOLUTION SYNTHESIS BASED ON WEIGHTED RESULTS FROM RANDOM FOREST CLASSIFIER
SYSTEMS AND METHODS FOR SUPER-RESOLUTION SYNTHESIS BASED ON WEIGHTED RESULTS FROM RANDOM FOREST CLASSIFIER
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机译：基于加权森林分类器加权结果的超分辨率综合系统和方法
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摘要
Methods and systems which provide super-resolution synthesis based on weighted results from a random forest classifier are described. Methods and systems apply a trained random forest classifier to low-resolution patches generated from the low-resolution input image to classify the low-resolution input patches. As each low-resolution patch is fed into the random forest classifier, each decision tree in the random forest classifier "votes" for a particular class for each of the low-resolution patches. Each class is associated with a projection matrix. The projection matrices output by the decision trees are combined by a weighted average to calculate an overall projection matrix corresponding to the random forest classifier output, which is used to calculate a high-resolution patch for each low-resolution patch. The high-resolution patches are combined to generate a synthesized high-resolution image corresponding to the low-resolution input image.
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