4KAgent: Agentic Any Image to 4K Super-Resolution

2025-07-09

AI Summary

This paper introduces the first autonomous agent paradigm tailored for low-level vision tasks, addressing the long-standing challenge of generalizable super-resolution (SR) reconstruction from arbitrarily severely degraded low-resolution images (e.g., 256×256) to 4K+ outputs. Methodologically, it proposes a perception–restoration dual-agent architecture: a perception agent leverages vision-language models to interpret image content and invoke domain-specific quality evaluators; a restoration agent employs recursive execute–reflect loops and a quality-driven mixture-of-experts strategy to dynamically select and orchestrate specialized submodules—including SR, denoising, and facial restoration—with a dedicated facial restoration pipeline ensuring fidelity in critical regions. Evaluated across 11 task categories and 26 benchmarks, the method achieves state-of-the-art performance on diverse modalities (natural, medical, and satellite imagery), significantly improving perceptual quality (LPIPS ↓) and fidelity (NIQE ↓, FID ↓).

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

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.

Problem

• Upscale any image to 4K resolution universally
• Restore highly degraded low-resolution images photorealistically
• Enhance facial details in portrait and selfie photos

Innovation

• Unified agentic super-resolution generalist system
• Perception Agent analyzes input with vision-language models
• Restoration Agent uses quality-driven mixture-of-expert policy