Paper Title
Creation of Animation-Like Backgrounds using Deep Learning

Abstract
Processing images using object detection, image restoration, and generative adversarial networks to directly convert real-world images into high-quality anime-style background images is one of today's research hotspots in computer vision. Input real-world images, object detection using the cutting-edge target detection algorithm DETR and generation of masks for the detected objects. The image restoration algorithm LaMa is then used to erase areas of the image with masked portions, generating a real-world background image.Finally, AnimeGAN generative adversarial network is used to convert the real world background image into anime style background image.Aiming at the current popular Anime GAN's problems such as color distortion in image migration, a new AnimeGAN-SE is proposed by introducing SE-Residual Block (Squeeze Excitation Residual Block) to solve the problem of low color of the migrated image of Anime GAN. The experimental results show that the network works well for animated pictures. Keywords - Object Detection, Image Restoration, Generative Adversarial Networks, Anime Style