MESA restores ancient inscription textures via multi-exemplar style transfer from VGG19 features with per-layer exemplar selection and OCR-derived weights, without any model training.
C.; Qiao, Y.; Tang, X
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Recoverability maps use synthetic sweeps of viewing angles and artifacts to quantify the recoverable fraction of parameter space for license plate restoration, with the best model succeeding on 93% and geometry setting the limit over architecture choice.
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MESA: A Training-Free Multi-Exemplar Deep Framework for Restoring Ancient Inscription Textures
MESA restores ancient inscription textures via multi-exemplar style transfer from VGG19 features with per-layer exemplar selection and OCR-derived weights, without any model training.
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Mapping License Plate Recoverability Under Extreme Viewing Angles for Oppor-tunistic Urban Sensing
Recoverability maps use synthetic sweeps of viewing angles and artifacts to quantify the recoverable fraction of parameter space for license plate restoration, with the best model succeeding on 93% and geometry setting the limit over architecture choice.