Restoration of motion-blurred star image based on improved MIMO-UNet
Jingyi Zhang, Zhaodong Niu · 2023
When observing space debris with ground-based telescopes, in some cases relative motion occurs between the star and the telescope, creating a trailing tail. Motion-blurred star images are characterized by low signal-to-noise ratio and heterogeneous motion blur. In order to improve the recovery quality of motion blurred star images, this paper constructs a motion blurred star image dataset using simulated star images. The Residual Block in the MIMO-UNet is replaced by Res FFT-Conv Block to extract the spatial domain and frequency domain information of the blurred star image. Finally, training is performed on the improved MIMO-UNet. The experimental results show that the network can improve the energy of the trailing stars and recover the shape of the trailing stars.