Leading compressed sensing (CS) methods require m = O (k log(n)) compressive samples to perfectly reconstruct a k-sparse signal x of size n using random projection matrices (e.g., ...
Super-resolution algorithms combine multiple low resolution images into a single high resolution image. They have received a lot of attention recently in various application domai...
Abstract—Image reconstruction from its projections is a necessity in many applications such as medical (CT), security, inspection, and others. This paper extends the 2-D Fan-beam...
This paper presents a method for scene flow estimation from a calibrated stereo image sequence. The scene flow contains the 3-D displacement field of scene points, so that the 2-D...
Compressed sensing(CS) suggests that a signal, sparse in some basis, can be recovered from a small number of random projections. In this paper, we apply the CS theory on sparse ba...
Dikpal Reddy, Aswin C. Sankaranarayanan, Volkan Ce...