DDRM: Diffusion Degradation Restoration Model / 扩散退化还原模型
Model Description / 模型简介
DDRM (Diffusion Degradation Restoration Model) is a deep learning framework engineered for high-precision image restoration and color alignment. By leveraging Reference-based Color Querying, the model achieves accurate restoration of complex degraded images based on external stylistic cues.
Cross-Image Semantic Alignment: Extracts high-quality color priors from reference images. / 跨图像语义对齐：从参考图中提取高质量的色彩先验信息。
Learnable Query Mechanism: Utilizes 100 learnable query vectors to "search" the reference feature space, automatically selecting the optimal color and lighting parameters for the target scene. / 查询向量机制：利用 100 个可学习的查询向量在参考图特征空间中进行“搜索”，自动拾取最契合当前场景的色彩与光影参数。
Global Color Anchors: Instead of relying solely on pixel values, the model analyzes the PDF (Probability Density Function) to ensure the restored image maintains global tonal consistency with the reference. / 全局色彩锚点：模型不再仅仅依赖像素值，而是通过分析概率密度函数 (PDF) 特征，确保还原图像在全局色调上与参考图保持统计学一致。
Hybrid Architecture: Combines the local fidelity of ConvNeXt V2 with the global receptive field of Transformers. / 混合架构：结合了 ConvNeXt V2 的局部保真度与 Transformer 的全局感受野。
Decoupled Representation Learning: The encoder extracts structure and texture, while the multi-scale Transformer decoder injects color information guided by the Query mechanism. / 解耦表征学习：编码器负责提取结构与纹理，而多尺度 Transformer 解码器负责注入由 Query 引导的色彩信息。
