online gambling singapore online gambling singapore online slot malaysia online slot malaysia mega888 malaysia slot gacor live casino malaysia online betting malaysia mega888 mega888 mega888 mega888 mega888 mega888 mega888 mega888 mega888 Recovering “lost dimensions” of images and video

摘要: Model could recreate video from motion-blurred images and “corner cameras,” may someday retrieve 3D data from 2D medical images.

 


MIT researchers have developed a model that recovers valuable data lost from images and video that have been “collapsed” into lower dimensions.

The model could be used to recreate video from motion-blurred images, or from new types of cameras that capture a person’s movement around corners but only as vague one-dimensional lines. While more testing is needed, the researchers think this approach could someday could be used to convert 2D medical images into more informative — but more expensive — 3D body scans, which could benefit medical imaging in poorer nations.

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In training, the researchers fed the CNN thousands of pairs of projections and their high-dimensional sources, called “signals.” The CNN learns pixel patterns in the projections that match those in the signals. Powering the CNN is a framework called a “variational autoencoder,” which evaluates how well the CNN outputs match its inputs across some statistical probability. From that, the model learns a “space” of all possible signals that could have produced a given projection. This creates, in essence, a type of blueprint for how to go from a projection to all possible matching signals.

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Full Text: MIT News

 

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