Core Viewpoint - The article discusses advancements in image restoration technology, particularly through artificial intelligence, which enhances the quality of low-resolution images and damaged photographs, making them clearer and more detailed [1][2]. Group 1: Technological Advancements - Image restoration technology has undergone three significant innovations: the introduction of deep convolutional networks in 2014, generative adversarial networks (GANs) in 2017, and diffusion generative models in 2023 [1]. - By 2025, a new intelligent restoration model called HYPIR will emerge, combining the strengths of GANs and diffusion models, offering faster processing speeds and improved restoration quality [1]. Group 2: Model Features - HYPIR boasts three main features: it is significantly faster than previous technologies, capable of generating 8K ultra-high-definition details, and can perform personalized intelligent restoration based on user prompts [1]. - The model is adaptable, effectively handling various scenarios and types of image damage [1]. Group 3: Restoration Process - The restoration process involves three steps: first, a deep learning encoder cleans the image by removing blurriness and compression artifacts, trained on millions of "flawed-perfect" image pairs [2]. - The second step adds natural details using a multimodal large model that interprets the image content and generates corresponding textual descriptions for further enhancement [2]. - The final step employs adversarial training, where a generator creates restored images and a discriminator evaluates their realism, leading to continuous improvement of both components [3]. Group 4: Applications - Image restoration technology is increasingly integrated into various fields, including mobile photography, television broadcasting, high-definition displays, historical image restoration, and scientific image processing, highlighting its practical relevance in everyday life [3].