Abstract:

In view of the shortcomings of existing image inpainting methods in handling complex damage and relying on paired training samples， an end-to-end dual-stage self-supervised image inpainting method was proposed. The method included degradation simulation， edge restoration， and color reconstruction； synergistic restoration of structure and color was achieved through cooperative optimization. Experiments were conducted on a historical image dataset from archives， and evaluation was performed using four metrics： peak signal-to-noise ratio（PSNR）， learned perceptual image patch similarity（LPIPS）， Fréchet inception distarce（FID）and Colorfulness Scare. Experimental results demonstrate that the proposed method outperforms existing mainstream methods in terms of image restoration accuracy， perceptual quality， and color performance， exhibiting good practical value and robustness.

Key words: image inpainting, self-supervised learning, image degradation simulation, cooperative optimization, attribute disentanglement, deep learning