  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.
