  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.
