The invention discloses an unsupervised
reconstruction method for adaptive
optical image restoration, which is applied to the technical field of astronomical observation and aims to solve the problem that in the prior art, the imaging resolution is reduced due to atmospheric turbulence in the observation of a ground-based
solar telescope. According to the method, multi-frame
blind deconvolution is creatively fused into an unsupervised
deep learning framework, an
encoder-decoder network is constructed to generate a potential clear image, meanwhile, a depth priori condition network is constructed to optimize a fuzzy kernel, and efficient
recovery is achieved by solving network parameters and the multi-frame
blind deconvolution problem through alternate iteration. A GCM model is introduced into a corrector to ensure stable convergence, and a BM3
D algorithm is adopted in the denoising process. Compared with the prior art, the method does not need a large amount of training data, is high in generalization capability, is high in
recovery quality, can process non-isoplanatic and multiband images, achieves the complementary advantages of a conventional
algorithm and
deep learning, is high in calculation efficiency, is high in practicality, and provides a more advanced and reliable
image processing technology for the fields of astronomical observation and the like.