Nonlinear image reconstruction based upon sparse representations of images has recently received widespread attention with the emerging framework of compressed sensing (CS). This ...
Roummel F. Marcia, Zachary T. Harmany, Rebecca Wil...
In [12] the authors proved an asymptotic sampling theorem for sparse signals, showing that n random measurements permit to reconstruct an N-vector having k nonzeros provided n >...
The Compressive Sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by reducing the sampling rate required to acquire and stably recover sparse s...
Laurent Jacques, Jason N. Laska, Petros Boufounos,...
This paper describes a method for establishing dense correspondence between two images in a video sequence (motion) or in a stereo pair (disparity) in case of large displacements....
Moustapha Kardouchi, Janusz Konrad, Carlos V&aacut...
We report initial results on shortening propositional resolution refutation proofs. This has an application in speeding up deductive reconstruction (in theorem provers) of large p...