INRetouch: 基于上下文的隐式神经表示用于摄影修图
INRetouch: Context Aware Implicit Neural Representation for Photography Retouching
AI总结：
INRetouch通过基于上下文的隐式神经表示，实现高保真度的摄影修图，并提升图像重建性能。
摘要：
专业摄影编辑仍然具有挑战性，需要对成像管线有广泛的了解和显著的专业知识。尽管最近的深度学习方法，特别是风格迁移方法，试图自动化这一过程，但它们在输出保真度、编辑控制和复杂修图能力方面常常遇到困难。我们提出了一种新的修图迁移方法，通过前后图像对学习专业编辑，从而能够精确复制复杂的编辑操作。我们开发了一种基于上下文的隐式神经表示，能够根据图像内容和上下文适应性地应用编辑，并能够从单个示例中学习。我们的方法从参考编辑中提取隐式转换，并适应性地应用于新图像。为了促进这一研究方向，我们引入了一个包含100,000张高质量图像的全面摄影修图数据集，这些图像使用超过170个专业Adobe Lightroom预设进行编辑。通过广泛的评估，我们证明我们的方法不仅在摄影修图方面超过了现有方法，还提高了相关图像重建任务如色域映射和RAW重建的性能。通过弥合专业编辑能力和自动化解决方案之间的差距，我们的工作为使复杂的摄影编辑更加可访问，同时保持高保真度的结果迈出了重要一步。源代码和数据集可在https://omaralezaby.github.io/inretouch 公开获取。
英文摘要：
Professional photo editing remains challenging, requiring extensive knowledge of imaging pipelines and significant expertise. While recent deep learning approaches, particularly style transfer methods, have attempted to automate this process, they often struggle with output fidelity, editing control, and complex retouching capabilities. We propose a novel retouch transfer approach that learns from professional edits through before-after image pairs, enabling precise replication of complex editing operations. We develop a context-aware Implicit Neural Representation that learns to apply edits adaptively based on image content and context, and is capable of learning from a single example. Our method extracts implicit transformations from reference edits and adaptively applies them to new images. To facilitate this research direction, we introduce a comprehensive Photo Retouching Dataset comprising 100,000 high-quality images edited using over 170 professional Adobe Lightroom presets. Through extensive evaluation, we demonstrate that our approach not only surpasses existing methods in photo retouching but also enhances performance in related image reconstruction tasks like Gamut Mapping and Raw Reconstruction. By bridging the gap between professional editing capabilities and automated solutions, our work presents a significant step toward making sophisticated photo editing more accessible while maintaining high-fidelity results. The source code and the dataset are publicly available at https://omaralezaby.github.io/inretouch .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
