We introduce a new approach to image reconstruction from highly incomplete data. The available data are assumed to be a small collection of spectral coef?cients of an arbitrary li...
Karen O. Egiazarian, Alessandro Foi, Vladimir Katk...
In this paper we show that, surprisingly, it is possible to recover sparse signals from nonlinearly distorted measurements, even if the nonlinearity is unknown. Assuming just that...
This article extends the concept of compressed sensing to signals that are not sparse in an orthonormal basis but rather in a redundant dictionary. It is shown that a matrix, whic...
Holger Rauhut, Karin Schnass, Pierre Vandergheynst
We present a CMOS imager with built-in capability to perform Compressed Sensing coding by Random Convolution. It is achieved by a shift register set in a pseudo-random configurat...
Laurent Jacques, Pierre Vandergheynst, Alexandre B...
This paper addresses the image representation problem in visual sensor networks. We propose a new image representation scheme based on compressive sensing (CS) because compressive...