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Basic Information

4KAgent is an agentic image super-resolution framework designed to universally upscale any input image to 4K resolution. The project aims to handle a wide range of image categories and degradation levels including classical and realistic degradations, extreme low-quality inputs, AI-generated imagery, remote sensing, microscopy, and biomedical images. It describes a multi-agent system architecture with a Perception Agent that analyzes content and distortion and a Restoration Agent that executes recursive restoration plans with execution, reflection, and rollback. The repository also introduces a Quality-Driven Mixture-of-Expert policy, a face restoration pipeline, a Profile Module for adapting the system to different restoration tasks without extra training, and a DIV4K-50 dataset for evaluation. The README references an accompanying research paper and notes that code is coming soon.

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Features
The README highlights several core features: a first-of-its-kind agentic framework specifically for any-image-to-4K upscaling, a multi-agent system split into Perception and Restoration Agents, and the use of large vision-language models in perception to generate restoration plans. It proposes a Quality-Driven Mixture-of-Expert policy (Q-MoE) to select optimal outputs during execution and reflection. It includes a dedicated face restoration pipeline to enhance facial regions. A Profile Module enables customization for different restoration tasks without retraining. The project provides the DIV4K-50 dataset as a challenging testset for upscaling 256√ó256 low-quality images to 4096√ó4096 high-quality images. The README positions the work as research-focused and notes code availability is forthcoming.
Use Cases
4KAgent helps researchers and practitioners by providing a structured, agent-based approach to robust image upscaling that adapts to diverse input types and degradations. The Perception Agent produces targeted restoration plans informed by vision-language analysis, helping the system choose suitable operations for each image. The Restoration Agent’s execution-reflection-rollback loop supports iterative improvement and error correction. The Q-MoE policy aids selection of higher-quality results during each restoration step. The face restoration pipeline improves results for images containing people. The Profile Module allows users to tailor behavior to specific tasks or domains without additional model training. The DIV4K-50 dataset offers a standardized benchmark for evaluating extreme upscaling performance. The README indicates associated research documentation and that implementation code will be released.

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