Lenses have been a cornerstone of optical systems for centuries; however, they are inherently limited by the laws of physics, particularly in terms of size and weight. Because of their characteristic light weight, small size, and subwavelength modulation, metalenses have the potential to miniaturize and integrate imaging systems. However, metalenses still face the problem that chromatic aberration affects the clarity and accuracy of images. A high-quality image system based on the end-to-end joint optimization of a neural network and an achromatic metalens is demonstrated in this paper. In the multi-scale encoder-decoder network, both the phase characteristics of the metalens and the hyperparameters of the neural network are optimized to obtain high-resolution images. The average peak-signal-to-noise ratio (PSNR) and average structure similarity (SSIM) of the recovered images reach 28.53 and 0.83. This method enables full-color and high-performance imaging in the visible band. Our approach holds promise for a wide range of applications, including medical imaging, remote sensing, and consumer electronics.
Keywords: end-to-end joint neural network; high-quality image system; metalens.