Fast Iterative Shrinkage for Signal Declipping and Dequantization

Abstract

We address the problem of recovering a sparse signal from clipped or quantized measurements. We show how these two problems can be formulated as minimizing the distance to a convex feasibility set, which provides a convex and differentiable cost function. We then propose a fast iterative shrinkage/thresholding algorithm that minimizes the proposed cost, which provides a fast and efficient algorithm to recover sparse signals from clipped and quantized measurements.

in Proceedings of iTWIST'18, Paper-ID: 4, Marseille, France, November, 21-23, 2018