CS: Basis Pursuit DeQuantizer, BPDQ Toolbox now available.

David Hammond, Laurent Jacques and M. Jalal Fadili just released the BPDQ Toolbox. From the page:

This toolbox aims at demonstrating the power of the Basis Pursuit Dequantizers for recovery of sparse signals from quantized random measurements, as described in the paper

"Dequantizing Compressed Sensing : When Oversampling and Non-Gaussian Constraints Combine" (preprint)

by Laurent Jacques, David Hammond, M. Jalal Fadili, May 2009. ( submitted to IEEE Transactions on Signal Processing)

More precisely, this toolbox provides a set of matlab and C/C++ routines solving numerically the two following convex optimization frameworks:

In other words, the L1-based and the Total Variation (TV) based Basis Pursuit Dequantizers.

of particular interest:

HTML reports of these demonstrations (automatically generated by Matlab 7.5) are also available here:

• Demo 1D Report
• Demo 2D Report

This is the first of its kinds. Thank you David for the heads-up. I'll include it in thee reconstruction section of the Big Picture in Compressive Sensing later.