发表机构
Fraunhofer IGD; Norwegian University of Science and Technology（弗劳恩霍夫应用研究促进协会图形数据处理研究所; 挪威科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出人机交互深度学习框架，结合专家指导、可编辑中间表示与纹理保留后处理，解决 Lenticular 胶片色彩重建难题，实现高质量可展示的色彩重建，推动领域技术前沿。
AI 中文摘要
Kodacolor 工艺制作的历史 Lenticular 胶片以独特的空间格式编码色彩信息，该结构需要专门技术才能实现准确的色彩重建。尽管 doLCE 等近期信号处理方法以及 deep-doLCE 等深度学习方法在自动化色彩恢复方面取得了进展，但它们在面对弯曲微透镜（lenticules）、低对比度或拍摄质量差的区域等情况时往往失效。我们提出了一种专为 Lenticular 胶片色彩重建设计的人机交互（Human-in-the-Loop, HITL）深度学习框架。该方法引入了可编辑的基于向量的微透镜边界表示，允许专家在色彩提取和去马赛克之前交互式地优化边界位置。这种解耦架构支持针对性校正和迭代微调，将专家知识嵌入检测模型，提升了对具有挑战性帧的鲁棒性。为了仅利用原始银乳剂中存在的信息保留图像细节，我们将重建的色度与原始胶片扫描的亮度合并。我们在一个具有挑战性的 Lenticular 胶片序列上评估了我们的流程，该序列是先前自动化方法失效且重建色彩不适合展示的情况。相比之下，我们的 HITL 方法成功生成了保留纹理的高质量、可展示的色彩重建结果。本研究首次将专家指导、可编辑的中间表示和纹理保留后处理相结合，用于 Lenticular 胶片的色彩重建，推动了该领域的技术前沿。
英文摘要
Historical lenticular films, such as those created with the Kodacolor process, encode color information in a distinctive spatial format. This structure requires specialized techniques for accurate color reconstruction. While recent signal processing approaches like doLCE and deep learning methods like deep-doLCE have advanced automated color recovery, they often fail with cases such as curved lenticules, low-contrast, or badly captured regions. We propose a human-in-the-loop (HITL) deep learning framework which is designed for color reconstruction in lenticular films. Our approach introduces an editable, vector-based representation of lenticule boundaries, allowing experts to interactively refine boundary positions before color extraction and demosaicing. This decoupled architecture enables targeted corrections and iterative fine-tuning, embedding expert knowledge into the detection model and improving robustness across challenging frames. To preserve image details using information solely present in the original silver emulsion, we merge the reconstructed chrominance with the original film scan's luminance. We evaluate our pipeline on a challenging lenticular film sequence where previous automated approaches fail and the reconstructed colors are not suitable for exhibition. In contrast, our HITL approach successfully produces high-quality, exhibitable color reconstructions with preserved texture. This work is the first to combine expert guidance, editable intermediate representations, and texture-preserving post-processing for lenticular film color reconstruction, advancing the state of the art in this field.
