Kavli Affiliate: Lihong V. Wang
| First 5 Authors: Yushen Zuo, Yushen Zuo, , ,
| Summary:
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
256×256, 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.
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