Reconstruction of compressively sensed images via neurally plausible local competitive algorithms

We develop neurally plausible local competitive algorithms (LCAs) for reconstructing compressively sensed images. Reconstruction requires solving a sparse approximation problem. Our solution technique uses a neural network that emulates how the brain may actually solve such sparse approximation problems. This method could ultimately be implemented in analog electronics, which would not only significantly diminish processing time, but also enable analog implementations for both acquisition and reconstruction.