Fast Signal Recovery from Saturated Measurements by Linear Loss and Nonconvex Penalties
This work addresses saturation errors in compressive sensing systems, which is an incremental improvement for signal processing applications.
The paper tackles the problem of sparse signal recovery from saturated measurements in compressive sensing by proposing a linear loss approach with nonconvex penalties, resulting in significantly improved recovery performance and reduced computational time, as verified by numerical experiments.
Sign information is the key to overcoming the inevitable saturation error in compressive sensing systems, which causes information loss and results in bias. For sparse signal recovery from saturation, we propose to use a linear loss to improve the effectiveness from existing methods that utilize hard constraints/hinge loss for sign consistency. Due to the use of linear loss, an analytical solution in the update progress is obtained, and some nonconvex penalties are applicable, e.g., the minimax concave penalty, the $\ell_0$ norm, and the sorted $\ell_1$ norm. Theoretical analysis reveals that the estimation error can still be bounded. Generally, with linear loss and nonconvex penalties, the recovery performance is significantly improved, and the computational time is largely saved, which is verified by the numerical experiments.