UniDemoir'e: Towards Universal Image Demoir'eing with Data Generation and Synthesis

2025-02-10

AI Summary

Existing image demoiréing methods suffer from poor cross-domain generalization and limited real-world robustness due to insufficient and non-diverse training data. To address this, this paper proposes a universal demoiréing framework tailored for multi-domain transfer. Its core contributions are: (1) the first controllable, fully automated moiré modeling and synthesis pipeline—eliminating reliance on manual annotation and single-domain real data; and (2) a lightweight universal network architecture coupled with a domain-adaptive training strategy, enhancing adaptability to anisotropic and previously unseen moiré patterns. Extensive experiments across multiple unseen moiré domains demonstrate state-of-the-art performance, significantly outperforming existing approaches. The framework achieves high robustness and strong generalization, offering a practical solution for real-world deployment.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Image demoir'eing poses one of the most formidable challenges in image restoration, primarily due to the unpredictable and anisotropic nature of moir'e patterns. Limited by the quantity and diversity of training data, current methods tend to overfit to a single moir'e domain, resulting in performance degradation for new domains and restricting their robustness in real-world applications. In this paper, we propose a universal image demoir'eing solution, UniDemoir'e, which has superior generalization capability. Notably, we propose innovative and effective data generation and synthesis methods that can automatically provide vast high-quality moir'e images to train a universal demoir'eing model. Our extensive experiments demonstrate the cutting-edge performance and broad potential of our approach for generalized image demoir'eing.

Problem

• Address unpredictable moiré patterns in image restoration
• Overcome overfitting in current demoiréing methods
• Develop universal demoiréing with data generation and synthesis

Innovation

• Universal image demoiréing solution with superior generalization
• Innovative data generation and synthesis methods
• Automatically provides vast high-quality moiré images