arXiv 2603.148162026-03-16cs.CV

M2IR：通过Mamba风格调制和专家混合实现主动式全图像修复

M2IR: Proactive All-in-One Image Restoration via Mamba-style Modulation and Mixture-of-Experts

School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；School of Computer Science and Technology, Anhui University of Technology（安徽工业大学计算机科学与技术学院）；Shenzhen Ubiquitous Data Enabling Key Laboratory, Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院深圳 ubiquitous 数据赋能重点实验室）；College of Artificial Intelligence, Taiyuan University of Technology（太原科技大学人工智能学院）

Shiwei Wang, Yongzhen Wang, Bingwen Hu, Liyan Zhang, Xiao-Ping Zhang, Mingqiang Wei

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AI总结：

本文提出M2IR框架，通过编码阶段主动调节降质传播和解码阶段高效消除残余降质，提升图像修复的泛化能力与适应性。

摘要：

本文提出M2IR框架，通过编码阶段主动调节降质传播和解码阶段高效消除残余降质，提升图像修复的泛化能力与适应性。

英文摘要：

While Transformer-based architectures have dominated recent advances in all-in-one image restoration, they remain fundamentally reactive: propagating degradations rather than proactively suppressing them. In the absence of explicit suppression mechanisms, degraded signals interfere with feature learning, compelling the decoder to balance artifact removal and detail preservation, thereby increasing model complexity and limiting adaptability. To address these challenges, we propose M2IR, a novel restoration framework that proactively regulates degradation propagation during the encoding stage and efficiently eliminates residual degradations during decoding. Specifically, the Mamba-Style Transformer (MST) block performs pixel-wise selective state modulation to mitigate degradations while preserving structural integrity. In parallel, the Adaptive Degradation Expert Collaboration (ADEC) module utilizes degradation-specific experts guided by a DA-CLIP-driven router and complemented by a shared expert to eliminate residual degradations through targeted and cooperative restoration. By integrating the MST block and ADEC module, M2IR transitions from passive reaction to active degradation control, effectively harnessing learned representations to achieve superior generalization, enhanced adaptability, and refined recovery of fine-grained details across diverse all-in-one image restoration benchmarks. Our source codes are available at https://github.com/Im34v/M2IR.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。