Compressed Sensing

Abstract: A CMOS imager is presented which has the ability to perform localized compressive sensing on-chip. In-pixel convolutions of the sensed image with measurement matrices are computed in real time, and a proposed programmable two-dimensional scrambling technique guarantees the randomness of the coefficients used in successive observation.

Abstract: This short note studies a variation of the compressed sensing paradigm introduced recently by Vaswani et al., i.e., the recovery of sparse signals from a certain number of linear measurements when the signal support is partially known.

Gabriel Peyré - Aug 1, 2008 Dear Laurent, As far as the Lagrangian formulation is concerned, you can replace the iterative thresholding algorithm proposed by so many researcher (including Figueiredo Nowak and Daubechies et al) by proximal iterations, where the soft thresholding is replaced by some inner iterations of Chambolle ROF algorithm (published in JMIV).

Recently, I was using the SPGL1 toolbox to recover some “compressed sensed” images. As a reminder, SPGL1 implements the method described in “Probing the Pareto Frontier for basis pursuit solutions” of Michael P.