Researchers unveil a passive all-optical device using neural networks for asymmetric image transmission and transformation.
Key Details
- 1Janus meta-imager uses cascaded metasurfaces and diffractive neural networks for image control.
- 2Device can pass images unchanged in one direction and transform them (e.g., letters to icons) in the reverse direction.
- 3Operates passively at high speed (~10 kHz) with minimal energy consumption.
- 4Device size is compact: 0.5 × 0.5 mm²; validated in the near-infrared (800 nm) regime.
- 5Potential applications include optical encryption and direction-dependent data storage.
Why It Matters
The Janus meta-imager exemplifies how deep learning and novel optics can enable new imaging functions without electronics or active power, suggesting future advances for secure medical image transport, coding, and all-optical processing architectures in radiology and related fields.
Source
EurekAlert
Related News
•EurekAlert
NIH-Backed AI Model Predicts Cancer Survival Using Single-Cell Data
Researchers have developed scSurvival, a machine learning tool that uses single-cell tumor data to accurately predict cancer patient survival and identify high-risk cell populations.
•EurekAlert
AI Pathology Model Outperforms PD-L1 in Predicting NSCLC Immunotherapy Response
MD Anderson's Path-IO machine learning platform accurately predicts immunotherapy responses in metastatic non-small cell lung cancer, surpassing current biomarker standards.
•EurekAlert
Deep Learning Pathomics Platform Improves Immunotherapy Prediction in Lung Cancer
A deep learning pathomics platform accurately predicts immunotherapy response in metastatic NSCLC using routine pathology slides.