DEFOCUS DEGRADATION RESTORATION OF ASTRONOMICAL IMAGES BASED ON UNET+KG-*3+ GENERATIVE ADVERSARIAL NETWORK

Astronomical observations are often interfered, resulting in various types of degradation of the collected images, among which defocus blur and photoelectronic noise are common and complex. It is difficult to recover high quality images by traditional restoration methods. Therefore, the innovative method of using Unet++ to improve the generative adversarial network is proposed. The finer network structure was used to accurately extract the details of the image. Comparative experiments show that this method has higher quality of image restoration, and by restoring real defocus images, it proves that the method has a certain versatility. The improved method is suitable for processing astronomical images with large amount of data. Moreover, the generalization ability and training stability of the model are obviously improved.