用扩散模型提升红外图像超分辨率，更好保持图像结构与分布一致。
Decoupling Cross-Modality Manifold Discrepancy: Leveraging Visible Diffusion Priors for Infrared Super-Resolution
- 双路径扩散框架，分离全局分布与局部结构优化
- 在多个数据集上实现更高结构一致性与更优视觉质量
- 适合需要高保真红外图像重建的研究者或工程应用
红外图像超分辨率（IISR）可缓解低空间分辨率带来的限制。现有方法虽意识到需在增强清晰度的同时保持全局分布和结构信息的一致性，但普遍存在不足或过度干扰的问题，尤其在扩散模型中更为突出。为此，我们提出一种基于双路径扩散的IISR框架——Shift-IISR。该方法旨在提升结果的一致性，同时保留扩散模型的生成能力。具体地，设计全局表征调制（GRM）模块，从红外图像中提取模态特异性信息，引导扩散模型的全局分布逼近真实图像；引入局部结构精修（LSR）模块，在迭代去噪过程中聚焦于结构信息。大量实验表明，所提方法显著提升了分布与结构一致性，同时保持了有竞争力的超分辨率性能。代码已开源：https://github.com/Assassink8/Shift-IISR。
原文摘要 · Abstract (English)
Infrared image super-resolution (IISR) mitigates the limitations imposed by low spatial resolution. Existing methods have recognized that IISR should preserve consistency in global distribution and structural information while enhancing image clarity. However, these methods are either insufficient or overly intrusive, a problem that becomes even more pronounced in diffusion-based models. To address these issues, we propose a dual-path diffusion-based framework for IISR, termed Shift-IISR. The proposed method is designed to improve the consistency of IISR results while preserving the generative capacity of diffusion models. Specifically, we develop a Global Representation Modulation (GRM) module to extract modality-specific information from infrared imagery and guide the global distribution of the diffusion model toward the ground truth. In addition, we introduce a Local Structure Refinement (LSR) module to encourage the model to focus on structural information at each step of the iterative denoising process. Extensive experiments demonstrate that the proposed method effectively improves distributional and structural consistency while maintaining competitive super-resolution performance. The source code of the proposed Shift-IISR can be available at https://github.com/Assassink8/Shift-IISR.
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