Abstract Optical telescopes are an important tool for acquiring optical information about
distant objects, and resolution is an important indicator that measures the ability to observe
object details. However, due to the effects of system aberration, atmospheric vortex, and
other factors, the observation image of ground-based telescopes is often degraded, resulting in
reduced resolution. This paper proposes an optical-neural network joint optimization method
to improve the resolution of the observed image by co-optimizing the point spread function
(PSF) of the telescopic system and the image super-resolution network. To improve the speed
of image reconstruction, we designed a generative adversarial net (LCR-GAN) with light
parameters, which is much faster than the latest unsupervised networks. To reconstruct the
PSF trained by the network in the optical path, a phase mask is introduced. It improves the
image reconstruction effect of LCR-GAN by reconstructing the point spread function that
best matches the network. The results of simulation and verification experiments show that
compared with the pure deep learning method, the super-resolution image reconstructed by
this method is rich in detail and easier to distinguish stars or stripes.