An Intelligent Agentic System for Complex Image Restoration Problems

Kaiwen Zhu^*, Jinjin Gu^*, Zhiyuan You, Yu Qiao, Chao Dong

ICLR 2025

Paper | Project Page

Overview

Learning from exploration

Workflow

Examples

Restoration of real-world images

Restore a UDC image (from this work) by motion deblurring, defocus deblurring, and low light enhancement.

<div> <img src="assets/udc10_input.png" width="49%"/> <img src="assets/udc10_output.png" width="49%"/> </div>

Restore an underwater image (from this work) by defocus deblurring, dehazing, and motion deblurring.

<div> <img src="assets/9094_input.png" width="49%"/> <img src="assets/9094_output.png" width="49%"/> </div>

Effectiveness of planning with experience

Effectiveness of workflow designs

Installation

Please refer to INSTALL.md.

Usage

Fine-tuning DepictQA

Please refer to this.

Setup

• Fill in the API key in config.yml.
• Run python src/app_eval.py and python src/app_comp.py in the directory DepictQA.

Data preparation

To generate complexly degraded images, run python -m dataset.synthesize. You should place clean images in dataset/HQ/ and corresponding depth maps in dataset/depth/. In the paper we use the MiO100 dataset. The degradation combinations are listed in dataset/degradations.txt. You can customize combinations in dataset/degradations.txt or degradation types in dataset/add_single_degradation.py.

The data used in the paper can be downloaded from this link.

Learning

To let the agent learn from exploration, run

• python -m exploration.exhaust_seq to generate images to explore;
• python -m exploration.explore to accumulate experience by evaluating images;
• python -m exploration.distill to summarize the experience and distill knowledge.

Inference

Run python -m pipeline.infer to restore an image (path specified in pipeline/infer.py).

BibTex

@inproceedings{agenticir,
      title={An Intelligent Agentic System for Complex Image Restoration Problems},
      author={Kaiwen Zhu and Jinjin Gu and Zhiyuan You and Yu Qiao and Chao Dong},
      booktitle={The Thirteenth International Conference on Learning Representations},
      year={2025},
      url={https://openreview.net/forum?id=3RLxccFPHz}
}