No abstract

Free electrons and ions in the ionosphere can change the propagation speed and direction of radio waves, which greatly affects the performance of wireless communication, navigation, radar positioning, and so on (Ravan et al., 2012;Zhou et al., 2011). The effective extraction of ionospheric echoes is beneficial to the research of ionospheric characteristics, for example, the physical process, the characteristics of the ionospheric state, and the law of ionospheric radio wave propagation. In addition, it supports the study of radio transmission theory and provides a theoretical basis for the design, use and detection of the radio engineering system.Recently, some studies on the extraction of ionospheric echoes mainly focus on three types of ionograms, which are obtained by vertical, oblique and backscatter sounding (

： Aiming at the problem of image motion blur caused by hand-held camera jitter and object motion in the process of collecting photos, a generative adversarial network (GAN) based on feature fusion of back projection is proposed for blind image deblurring. Firstly, the generator network is established by using U-Net structure, and a feature fusion residual block based on back projection is designed according to the error feedback principle, which solves the problem of saving spatial information in U-Net structure. Secondly, the self-attention module is introduced into the generator network to extract the feature map that pays more attention to detail. Finally, the combination of perceptual loss, mean square error loss and relative generative adversarial loss effectively alleviates the mode collapse problem of traditional GAN and improves the stability of model training. The experimental results show that the peak signal to noise ratio (PSNR) and structural similarity (SSIM) of this method on GoPro data set are 30.183dB and 0.941 respectively, and 26.962 and 0.837 on the Kohler dataset, with the shortest running time, which are better than the existing mainstream methods. The restored image is clearer in subjective vision and richer in texture details, which can effectively improve the image deblurring effect.