Achromatic imaging systems with flat lenses enabled by deep learning

TL;DR

This paper presents a deep learning-based method to correct chromatic aberrations in flat lenses, enabling high-quality, full-spectrum imaging and overcoming a major limitation of flat lens technology.

Contribution

The study introduces a novel dataset and a deep learning model for chromatic aberration correction in flat lenses, achieving unprecedented imaging quality across the visible spectrum.

Findings

High PSNR and SSIM scores in reconstructed images

Effective correction of chromatic aberrations in flat lenses

Potential for flat lenses in advanced polychromatic imaging systems

Abstract

Motivated by their great potential to reduce the size, cost and weight, flat lenses, a category that includes diffractive lenses and metalenses, are rapidly emerging as key components with the potential to replace the traditional refractive optical elements in modern optical systems. Yet, the inherently strong chromatic aberration of these flat lenses is significantly impairing their performance in systems based on polychromatic illumination or passive ambient light illumination, stalling their widespread implementation. Hereby, we provide a promising solution and demonstrate high quality imaging based on flat lenses over the entire visible spectrum. Our approach is based on creating a novel dataset of color outdoor images taken with our flat lens and using this dataset to train a deep-learning model for chromatic aberrations correction. Based on this approach we show unprecedented imaging results not only in terms of qualitative measures but also in the quantitative terms of the PSNR and SSIM scores of the reconstructed images. The results pave the way for the implementation of flat lenses in advanced polychromatic imaging systems.