用可学习轨迹生成高质量图像特征，实现快速高质修复。
RestoRect: Degraded Image Restoration via Latent Rectified Flow & Feature Distillation
- 将特征蒸馏重构为潜空间中的生成过程，通过可学习轨迹学习教师特征。
- 在15个数据集上10项指标均优于基线，推理速度更快且训练更稳定。
- 适合需要高效高质图像修复的工业场景与实时应用。
当前图像修复方法面临性能与速度的权衡：高性能模型计算慢，快速模型效果差。知识蒸馏虽能传递教师模型知识，但现有静态特征匹配无法捕捉现代Transformer架构动态生成特征的机制。本文提出新型潜空间修正流特征蒸馏方法RestoRect，将特征蒸馏重新建模为生成过程，使学生模型通过潜空间中可学习轨迹合成教师级特征。框架结合Retinex分解、可学习各向异性扩散约束及三角色域极化。引入特征层提取损失，通过跨归一化Transformer特征对齐与百分位异常值检测实现跨架构鲁棒知识迁移。RestoRect在15个图像修复数据集（覆盖4类任务）上10项指标均超越基线，具备更好训练稳定性、更快收敛与推理速度，同时保持优异修复质量。
原文摘要 · Abstract (English)
Current approaches for restoration of degraded images face a trade-off: high-performance models are slow for practical use, while fast models produce poor results. Knowledge distillation transfers teacher knowledge to students, but existing static feature matching methods cannot capture how modern transformer architectures dynamically generate features. We propose a novel Latent Rectified Flow Feature Distillation method for restoring degraded images called \textbf{'RestoRect'}. We apply rectified flow to reformulate feature distillation as a generative process where students learn to synthesize teacher-quality features through learnable trajectories in latent space. Our framework combines Retinex decomposition with learnable anisotropic diffusion constraints, and trigonometric color space polarization. We introduce a Feature Layer Extraction loss for robust knowledge transfer between different network architectures through cross-normalized transformer feature alignment with percentile-based outlier detection. RestoRect achieves better training stability, and faster convergence and inference while preserving restoration quality, demonstrating superior results across 15 image restoration datasets, covering 4 tasks, on 10 metrics against baselines.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。