Image inpainting based on Mamba-GAN network

Image inpainting is an important research topic in the field of computer vision, aiming to fill in missing parts or repair damaged areas in images. Traditional image inpainting methods often rely on pixel interpolation or simple local information propagation. However, these approaches often fail to generate inpainted images with high consistency and natural appearance. To overcome these limitations, an image inpainting method based on the Mamba-GAN network was proposed in this paper, using the Mamba network as the generator and the Wasserstein GAN network as the discriminator, while designing various loss functions to optimize the generated results. The SSIM score of the images inpainted on the CelebA dataset reaches 0.9817, and the PSNR score reaches 39.7. For the Places2 dataset, the SSIM score reaches 0.9666, and the PSNR score reaches 33.84. The image restoration quality has significantly improved.