Close Encounters of the Binary Kind: Signal Reconstruction Guarantees for Compressive Hadamard Sampling with Haar Wavelet Basis

TL;DR

This paper provides explicit sample complexity bounds and recovery guarantees for reconstructing signals from subsampled Hadamard measurements using Haar wavelet sparsity, with applications in computational imaging.

Contribution

It introduces new theoretical bounds and guarantees for Hadamard-Haar sampling systems, addressing coherence issues in optical multiplexing and single-pixel imaging.

Findings

Explicit sample complexity bounds derived for Hadamard-Haar systems

Successful signal recovery demonstrated with few measurements in practical scenarios

Variable and multilevel sampling strategies improve reconstruction success

Abstract

We investigate the problems of 1-D and 2-D signal recovery from subsampled Hadamard measurements using Haar wavelet sparsity prior. These problems are of interest in, e.g., computational imaging applications relying on optical multiplexing or single-pixel imaging. However, the realization of such modalities is often hindered by the coherence between the Hadamard and Haar bases. The variable and multilevel density sampling strategies solve this issue by adjusting the subsampling process to the local and multilevel coherence, respectively, between the two bases; hence enabling successful signal recovery. In this work, we compute an explicit sample-complexity bound for Hadamard-Haar systems as well as uniform and non-uniform recovery guarantees; a seemingly missing result in the related literature. We explore the faithfulness of the numerical simulations to the theoretical results and show in a practically relevant instance, e.g., single-pixel camera, that the target signal can be obtained from a few Hadamard measurements.