Application Research on Image Recovery Technology Based on GAN.

Image restoration is a critical task in computer vision that involves recovering an original image from corrupted or damaged versions of it. Traditional image restoration relies on interpolation and completion techniques, such as Navier–Stokes equations and the fast multipole boundary element method. However, these methods often fail to capture the advanced semantic information of an image, leading to inaccurate restoration results. In recent years, generative adversarial networks (GANs) have emerged as a practical approach in computer vision for image restoration tasks, as they can address image restoration issues related to damaged or missing image information. GANs are a deep‐learning network structure with a generator and discriminator. In image restoration, the generator is employed to restore damaged images, and the discriminator is used to assess the authenticity of the repair results. Competition between the generator and discriminator improves the quality of the repair results. This study proposes a novel generative image restoration method that utilizes contextual and perceptual semantic information mechanisms to strengthen GANs. Our approach demonstrates the effectiveness of GANs in restoring images through learning how to fuse the missing or damaged parts of an image with the undamaged parts surrounding them, resulting in visually appealing restoration results. [ABSTRACT FROM AUTHOR]