High-resolution and ultra-low power nonlinear image processing with passive high-quality factor metasurfaces

We demonstrate a passive, all-optical image filter, achieving a leaky ReLU function with 0.1mW/μm2 thresholds and 1.85μm resolution. This filter offers an energy-free and adaptive nonlinear functionality to existing image processing.

Authors

Mark Lawrence (ORCID: https://orcid.org/0000-0002-0822-5453)
Andrew Samuel (ORCID: https://orcid.org/0009-0007-5470-6673)
Lin Lin (ORCID: https://orcid.org/0000-0002-1554-8883)
Bo Zhao (ORCID: https://orcid.org/0000-0003-0097-0077)

Publication Details

Published: 2025-01-01
DOI: https://doi.org/10.1364/cleo_fs.2025.ff131_4
Primary Topic: Neural Networks and Reservoir Computing
Type: article
Field-Weighted Citation Impact: 0.00

Controls

|||

ALL TIME

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

article

High-resolution and ultra-low power nonlinear image processing with passive high-quality factor metasurfaces

Mark Lawrence, Andrew Samuel, Lin Lin, Bo Zhao

Jan 1, 2025

Neural Networks and Reservoir Computing

article

High-resolution and ultra-low power nonlinear image processing with passive high-quality factor metasurfaces

Mark Lawrence, Andrew Samuel, Lin Lin, Bo Zhao

January 1, 2025article en

Abstract

Openalex Percentile: Top 87%

Neural Networks and Reservoir Computing

DOI Source OpenAlex

AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

High-resolution and ultra-low power nonlinear image processing with passive high-quality factor metasurfaces — Mark Lawrence, Andrew Samuel, et al. · (2025) | TGRS Research Map | TGRS