High-order mid-infrared nonlinear topological differentiator

TL;DR

This paper introduces a high-sensitivity mid-infrared differentiator that uses nonlinear topological patterns for real-time, high-contrast edge imaging, enhancing biomedical and material diagnostics.

Contribution

It demonstrates a novel MIR upconversion differentiator with topological complex-amplitude encoding, enabling tunable high-order edge enhancement at 3 μm with real-time operation.

Findings

Achieves isotropic high-order edge enhancement via nonlinear parametric interaction.

Enables tunable differentiation from first- to fourth-order at 60 Hz.

Provides high-contrast, low-light edge imaging with accurate background suppression.

Abstract

High-order edge-enhanced imaging enables precise feature localization and effective background suppression, offering a powerful tool for real-time recognition and high-contrast visualization. Extending this capability to the mid-infrared (MIR) regime is particularly valuable for applications such as biomedical diagnostics, material inspection, and remote sensing, yet remains limited by inadequate spatial-frequency modulation fidelity and low detection sensitivity. Here, we demonstrate a high-sensitivity MIR upconversion differentiator operating at 3 $\mu$m, which achieves isotropic high-order edge enhancement by optically imprinting topological complex-amplitude patterns onto MIR Fourier components via nonlinear parametric interaction. Vortex transfer functions $t(k_r, \phi) \propto k_r^\ell e^{i\ell\phi}$ are precisely encoded on a phase-only spatial light modulator to enable tunable MIR differentiation from first- to fourth- order, with real-time switching at up to 60 Hz. Benefiting from a low-noise upconversion process and a single-photon-sensitive silicon camera, the system achieves high-contrast edge imaging under low-light conditions. Experimental results confirm accurate edge extraction and background suppression for both amplitude and phase objects, hence underscoring its potential for noninvasive diagnostics and label-free material analysis.