High-fidelity achromatic metalens imaging via deep neural network

TL;DR

This paper introduces a deep learning approach to correct chromatic aberration in single metalens imaging, enabling achromatic, high-resolution, full-color images without complex hardware modifications.

Contribution

The work presents a novel deep neural network system that computationally refocuses and corrects chromatic aberration in metalens images, bypassing the need for complex optical engineering.

Findings

Effective chromatic aberration correction across multiple scenarios

Consistent image enhancement for different aperture sizes and distances

Successful reconstruction of real-world and out-of-training images

Abstract

Meta-optics are attracting intensive interest as alternatives to traditional optical systems comprising multiple lenses and diffractive elements. Among applications, single metalens imaging is highly attractive due to the potential for achieving significant size reduction and simplified design. However, single metalenses exhibit severe chromatic aberration arising from material dispersion and the nature of singlet optics, making them unsuitable for full-color imaging requiring achromatic performance. In this work, we propose and validate a deep learning-based single metalens imaging system to overcome chromatic aberration in varied scenarios. The developed deep learning networks computationally reconstruct raw imaging captures through reliably refocusing red, green and blue channels to eliminate chromatic aberration and enhance resolution without altering the metalens hardware. The networks demonstrate consistent enhancement across different aperture sizes and focusing distances. Images outside the training set and real-world photos were also successfully reconstructed. Our approach provides a new means to achieve achromatic metalenses without complex engineering, enabling practical and simplified implementation to overcome inherent limitations of meta-optics.