Noisy-Sudocodes - Software
This webpage describes the Matlab files used in our work on sparse signal reconstruction in Bayesian compressed sensing, as well as 1-bit compressed sensing, where the measurement matrix is the concatenation of a sparse binary matrix and a dense matrix with i.i.d. Gaussian entries. The construction of such a matrix allows us to design fast reconstruction algorithms; details appear in the following paper, The software was implemented by Yanting Ma. The implementation is a two-part reconstruction algorithm, where partial zero-valued entries are identified in Part 1, and the remaining entries are estimated by approximate message passing (AMP), which is proposed by Donoho et al., or binary iterative hard thresholding (BIHT), which is proposed by Jacques et al., in Part 2, for Bayesian compressed sensing problems and 1-bit compressed sensing problems, respectively.

Below is a brief description of the files used in our implementation.

Yanting Ma, January 2016


Bayesian compressed sensing problems

1-bit compressed sensing problems


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