SPColor：语义先验引导的基于示例的图像上色
SPColor: Semantic Prior Guided Exemplar-based Image Colorization
AI总结：
SPColor提出语义先验引导的示例上色框架，通过伪类别局部匹配和相似性掩码感知损失减少误匹配，无需人工标注即可在公开数据集上超越现有方法。
摘要：
基于示例的图像上色旨在根据一张彩色参考图像为目标灰度图像上色，其关键在于建立这两张图像之间准确的像素级语义对应关系。以往的方法在整个参考图像范围内搜索对应关系，这种全局匹配容易产生误匹配。我们将困难总结为两个方面：（1）当参考图像只包含与目标图像相关的部分物体时，会在不相关区域建立不恰当的对应关系。（2）在物体形状或纹理容易混淆的区域，容易产生误匹配。为了克服这些问题，我们提出了SPColor，一个语义先验引导的基于示例的图像上色框架。与以往方法不同，SPColor首先在语义先验的指导下将参考图像和目标图像的像素粗略分类为若干伪类别，然后通过新设计的语义先验引导对应网络，仅在相同类别的像素之间局部建立对应关系。通过这种方式，不同语义类别之间的不恰当对应被明确排除，误匹配得到明显缓解。此外，为了更好地保留参考图像中的颜色，我们设计了一种相似性掩码感知损失。值得注意的是，精心设计的SPColor利用了无监督分割模型提供的语义先验，无需额外的人工语义标注。实验表明，我们的模型在公开数据集上无论是定量还是定性都优于近期最先进的方法。
英文摘要：
Exemplar-based image colorization aims to colorize a target grayscale image based on a color reference image, and the key is to establish accurate pixel-level semantic correspondence between these two images. Previous methods search for correspondence across the entire reference image, and this type of global matching is easy to get mismatch. We summarize the difficulties in two aspects: (1) When the reference image only contains a part of objects related to target image, improper correspondence will be established in unrelated regions. (2) It is prone to get mismatch in regions where the shape or texture of the object is easily confused. To overcome these issues, we propose SPColor, a semantic prior guided exemplar-based image colorization framework. Different from previous methods, SPColor first coarsely classifies pixels of the reference and target images to several pseudo-classes under the guidance of semantic prior, then the correspondences are only established locally between the pixels in the same class via the newly designed semantic prior guided correspondence network. In this way, improper correspondence between different semantic classes is explicitly excluded, and the mismatch is obviously alleviated. Besides, to better reserve the color from reference, a similarity masked perceptual loss is designed. Noting that the carefully designed SPColor utilizes the semantic prior provided by an unsupervised segmentation model, which is free for additional manual semantic annotations. Experiments demonstrate that our model outperforms recent state-of-the-art methods both quantitatively and qualitatively on public dataset.
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。
