Super-resolution imaging of telescopic systems based on optical-neural network joint optimization

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.