4KAgent：智能体任意图像至4K超分辨率
4KAgent: Agentic Any Image to 4K Super-Resolution
AI总结：
4KAgent是一个智能体超分辨率系统，通过分析、感知与修复三模块，将任意低分辨率图像提升至4K，在26个基准上达到最先进水平，并集成人脸修复。
摘要：
我们提出了4KAgent，一个统一的智能体超分辨率通用系统，旨在将任意图像普遍提升至4K分辨率（若迭代应用，甚至可更高）。我们的系统能将极低分辨率且严重退化的图像，例如256x256的高度失真输入，转化为清晰逼真的4K输出。4KAgent包含三个核心组件：（1）分析模块，根据特定用例定制4KAgent流程；（2）感知智能体，利用视觉语言模型与图像质量评估专家分析输入图像并制定定制化修复计划；（3）修复智能体，执行该计划，遵循递归执行-反思范式，由质量驱动的专家混合策略指导，为每一步选择最优输出。此外，4KAgent嵌入了一个专门的人脸修复流程，显著增强肖像和自拍照片中的面部细节。我们在涵盖26个不同基准的11个不同任务类别上严格评估了4KAgent，在广泛的成像领域设立了新的最先进水平。我们的评估覆盖自然图像、肖像照片、AI生成内容、卫星图像、荧光显微镜以及医学成像（如眼底检查、超声和X射线），在感知（如NIQE、MUSIQ）和保真度（如PSNR）指标上均展现出优越性能。通过为低级视觉任务建立新颖的智能体范式，我们旨在促进不同研究社区中对视觉中心自主智能体的更广泛兴趣和创新。我们将发布所有代码、模型和结果，网址为：https://4kagent.github.io。
英文摘要：
We present 4KAgent, a unified agentic super-resolution generalist system designed to universally upscale any image to 4K resolution (and even higher, if applied iteratively). Our system can transform images from extremely low resolutions with severe degradations, for example, highly distorted inputs at 256x256, into crystal-clear, photorealistic 4K outputs. 4KAgent comprises three core components: (1) Profiling, a module that customizes the 4KAgent pipeline based on bespoke use cases; (2) A Perception Agent, which leverages vision-language models alongside image quality assessment experts to analyze the input image and make a tailored restoration plan; and (3) A Restoration Agent, which executes the plan, following a recursive execution-reflection paradigm, guided by a quality-driven mixture-of-expert policy to select the optimal output for each step. Additionally, 4KAgent embeds a specialized face restoration pipeline, significantly enhancing facial details in portrait and selfie photos. We rigorously evaluate our 4KAgent across 11 distinct task categories encompassing a total of 26 diverse benchmarks, setting new state-of-the-art on a broad spectrum of imaging domains. Our evaluations cover natural images, portrait photos, AI-generated content, satellite imagery, fluorescence microscopy, and medical imaging like fundoscopy, ultrasound, and X-ray, demonstrating superior performance in terms of both perceptual (e.g., NIQE, MUSIQ) and fidelity (e.g., PSNR) metrics. By establishing a novel agentic paradigm for low-level vision tasks, we aim to catalyze broader interest and innovation within vision-centric autonomous agents across diverse research communities. We will release all the code, models, and results at: https://4kagent.github.io.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
