Recovering "lost dimensions" of images and video

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. "In all these cases, the visual data has one dimension -- in time or space -- that's completely lost," says Guha Balakrishnan, a postdoc in Computer Science and Artificial Intelligence Laboratory (CSAIL) and first author on a paper describing the model, which is being presented at next week's International Conference on Computer Vision. "If we recover that lost dimension, it can have a lot of important applications." Captured visual data often collapses data of multiple dimensions of time and space into one or two dimensions, called "projections."