Fast Iterative Shrinkage for Signal Declipping and Dequantization

TL;DR

This paper introduces a fast iterative shrinkage algorithm for efficiently recovering sparse signals from clipped or quantized measurements by formulating the problem as a convex feasibility set minimization.

Contribution

It presents a novel convex and differentiable cost function formulation for declipping and dequantization, along with an efficient iterative shrinkage algorithm for signal recovery.

Findings

The proposed method achieves fast convergence in signal recovery.

It effectively handles both clipping and quantization distortions.

The approach outperforms existing methods in speed and accuracy.

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.