Improve image repair efficiency and quality based on Diffusion-GANs model

Abstract

With the rapid development of modern science and technology, deep learning and artificial intelligence have greatly promoted the progress of computer vision, among which image restoration is a key field of computer vision, aiming to repair images that are damaged or missing important parts of the main body. Although traditional interpolation and region filling techniques are effective in some environments, they often have difficulty handling complex scenes that require image restoration in today's world. In contrast, modern methods such as GANs and Diffusion models have significantly improved the quality and reliability of restoration. However, GANs are hindered by problems such as instability and mode collapse, and although diffusion models can generate high-quality images, they are computationally demanding. To address these challenges, this review explores the hybrid diffusion GANs framework and focuses on the basic conceptual restoration principles of the above three models and compares the performance of the GANs and Diffusion model and other traditional models by comparing indicators such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Furthermore, although Diffusion-GANs model is mainly applied to image generation, we discuss their great potential for image inpainting, providing new possibilities for future improvements in image restoration and generation.