Elucidating and Endowing the Diffusion Training Paradigm for General Image Restoration

2025-06-26

AI Summary

Diffusion models exhibit strong generative capabilities in image restoration (IR), yet their complex architectures and iterative inference hinder practical deployment within mainstream reconstruction-based IR networks. This work focuses on adapting the diffusion training paradigm to generic IR frameworks, addressing its transferability challenges in both single-task generalization and multi-task unified restoration. We systematically uncover the adaptation mechanisms governing temporal dependencies, network layer alignment, and noise distribution matching—revealing these relationships for the first time. To this end, we propose: (i) diffusion objective alignment regularization to bridge the gap between diffusion and deterministic restoration objectives; (ii) an incremental diffusion training paradigm that progressively incorporates diffusion priors without disrupting convergence; and (iii) a lightweight task-adaptive adapter enabling plug-and-play integration. Crucially, our method requires no backbone modification. It significantly improves single-task generalization and achieves state-of-the-art performance across diverse multi-task IR benchmarks.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

While diffusion models demonstrate strong generative capabilities in image restoration (IR) tasks, their complex architectures and iterative processes limit their practical application compared to mainstream reconstruction-based general ordinary IR networks. Existing approaches primarily focus on optimizing network architecture and diffusion paths but overlook the integration of the diffusion training paradigm within general ordinary IR frameworks. To address these challenges, this paper elucidates key principles for adapting the diffusion training paradigm to general IR training through systematic analysis of time-step dependencies, network hierarchies, noise-level relationships, and multi-restoration task correlations, proposing a new IR framework supported by diffusion-based training. To enable IR networks to simultaneously restore images and model generative representations, we introduce a series of regularization strategies that align diffusion objectives with IR tasks, improving generalization in single-task scenarios. Furthermore, recognizing that diffusion-based generation exerts varying influences across different IR tasks, we develop an incremental training paradigm and task-specific adaptors, further enhancing performance in multi-task unified IR. Experiments demonstrate that our method significantly improves the generalization of IR networks in single-task IR and achieves superior performance in multi-task unified IR. Notably, the proposed framework can be seamlessly integrated into existing general IR architectures.

Problem

• Adapt diffusion training paradigm to general image restoration
• Align diffusion objectives with image restoration tasks
• Enhance performance in multi-task unified image restoration

Innovation

• Adapts diffusion training to general IR frameworks
• Introduces regularization for diffusion-IR task alignment
• Develops incremental training for multi-task IR