发表机构
University of Science and Technology Beijing（北京科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对红外图像超分辨率问题，提出双路径基于扩散的Shift-IISR框架，通过开发GRM模块提取特定模态信息、引入LSR模块关注结构信息，有效提高了分布和结构一致性，保持了超分辨率性能。
AI 中文摘要
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IISR在增强图像清晰度时应保持全局分布和结构信息的一致性，但存在不足或过度干扰问题，在基于扩散的模型中更突出。为此提出双路径基于扩散的IISR框架Shift-IISR，旨在提高IISR结果的一致性并保留扩散模型的生成能力。具体开发了全局表示调制（GRM）模块从红外图像中提取特定模态信息，引导扩散模型的全局分布趋向真实图像；还引入局部结构细化（LSR）模块促使模型在迭代去噪过程的每一步关注结构信息。大量实验表明该方法有效提高了分布和结构一致性，同时保持了有竞争力的超分辨率性能。
英文摘要
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.
CommentsAccepted to ACM Multimedia 2026 (ACM MM 2026). Code: https://github.com/Assassink8/Shift-IISR
