Reconstruction from Periodic Nonlinearities, With Applications to HDR Imaging

TL;DR

This paper introduces a measurement scheme for reconstructing signals and images from periodic nonlinearities, enhancing HDR imaging by reducing measurement complexity with minimal quality loss, supported by numerical experiments.

Contribution

It presents a novel measurement scheme for efficient reconstruction from periodic nonlinearities, adaptable to compressive sensing, improving HDR imaging systems.

Findings

Effective reconstruction with reduced measurements

Adaptability to compressive sensing systems

Numerical experiments confirm performance

Abstract

We consider the problem of reconstructing signals and images from periodic nonlinearities. For such problems, we design a measurement scheme that supports efficient reconstruction; moreover, our method can be adapted to extend to compressive sensing-based signal and image acquisition systems. Our techniques can be potentially useful for reducing the measurement complexity of high dynamic range (HDR) imaging systems, with little loss in reconstruction quality. Several numerical experiments on real data demonstrate the effectiveness of our approach.