Compressive Sensing (CS) is an innovative process of acquiring and reconstructing a signal that is sparse or compressible. Around 2004 Emmanuel Candès, Terence Tao and David Donoho discovered important results on the minimum number of data needed to reconstruct an image even though the number of data would be deemed insufficient by the Nyquist–Shannon criterion. Starting in 2004, Professors Rich Baraniuk and Kevin Kelly of Rice University pioneered the application of CS to the creation of actual cameras and developed the “single-pixel” camera technique employing over $10M in government funding.
COMPRESSIVE SENSING CAMERAS
A Compressive Sensing camera replaces the spatial measurements normally made by a conventional of a Focal Plane Array (FPA) with a series of temporal measurements made by one or a small number of detector diodes. This significantly reduces the cost of infrared cameras. The temporal measurements are fed into an algorithm that reconstructs the spatial information of the original image. Read more »