不管是包浆的旧照、AI生成的低清图，又或是遥感/医学图像，现在都能智能修复并放大到4K超清分辨率。 图像清晰度提升一直是计算机视觉的"老问题"，面对噪声、模糊、压缩损伤等复合退化，以及AI合成、遥感、生物医 学等其他领域图像时，传统单一模型往往力不从心。 由德克萨斯A&M大学、斯坦福大学、Snap公司、CU Boulder大学、德克萨斯大学奥斯汀分校、加州理工大学、Topaz Labs以及加州大学Merced分校的研究者联合提出的基于AI智能体的方法4KAgent针对不同类型的图像以及需求对图像 进行智能修复并放大到4K分辨率，带来优秀的视觉感知效果。该工作已被NeurIPS 2025接收。 为什么当前图像放大技术不够用？ 传统图像放大模型通常只在特定类型图片上表现良好，一旦遇到真实世界中的复杂模糊、AI生成图的伪影，或是遥 感、医学等专业图像，就显得力不从心。 而将分辨率放大到4K，更是对细节重建和纹理真实度都提出了极高要求。 而在多数用户角度来看，有一个既通用又可控的框架来解决各式图像提升分辨率的需要是再好不过的。4KAgent正是 在这些现实挑战与需求下诞生的。 基于多智能体的设计，4KAgent可以为 ...

Core Insights - The article discusses the development of 4KAgent, an AI-based system designed to intelligently restore and upscale images to 4K resolution, addressing the limitations of traditional image enhancement methods [3][6][28] Group 1: Technology Overview - 4KAgent utilizes a multi-agent design to create tailored pathways for each image to achieve 4K resolution, enhancing visual perception [6][7] - The system incorporates a perception agent that analyzes image content and degradation information, generating a restoration plan based on various quality metrics [10][11] - The restoration agent employs an "execution-reflection-rollback" mechanism to iteratively optimize the restoration process, ensuring high-quality outputs [12][16] Group 2: Functionality and Features - 4KAgent supports nine different restoration tasks, utilizing state-of-the-art models to generate multiple candidate images for evaluation [13][14] - A face restoration module is integrated to specifically enhance facial details, ensuring high-quality results for images containing human faces [18] - The configuration module allows users to customize preferences for different restoration scenarios without requiring additional training [20] Group 3: Performance and Testing - 4KAgent has been extensively tested across 11 different super-resolution tasks and 26 benchmark datasets, demonstrating superior detail and accuracy in restored images [21][27] - In challenging scenarios, such as 16x upscaling, 4KAgent consistently produces high-detail and realistic textures, showcasing its effectiveness in various applications [25][27] - The system exhibits excellent generalization capabilities, performing well across diverse fields including natural scenes, portraits, AI-generated content, and scientific imaging [28]