Adaptive recovery of dictionary-sparse signals using binary measurements

Abstract

One-bit compressive sensing (CS) is an advanced version of sparse recovery in which the sparse signal of interest can be recovered from extremely quantized measurements. Namely, only the sign of each measure is available to us. The ground-truth signal is not sparse in many applications yet can be represented in a redundant dictionary. A strong line of research has addressed conventional CS in this signal model, including its extension to one-bit measurements. However, one-bit CS suffers from an extremely large number of required measurements to achieve a predefined reconstruction error level. A common alternative to resolve this issue is to exploit adaptive schemes. We utilize an adaptive sampling strategy to recover dictionary-sparse signals from binary measurements in this work. A multi-dimensional threshold is proposed for this task to incorporate the previous signal estimates into the current sampling procedure. This strategy substantially reduces the required number of measurements for exact recovery. We show that the proposed algorithm considerably outperforms the state-of-the-art approaches through rigorous and numerical analysis.