论文标题
内部多样的图像完成
Internal Diverse Image Completion
论文作者
论文摘要
图像完成广泛用于照片修复和编辑应用中，例如用于删除对象。最近，关于为丢失地区产生各种完成的研究激增。但是，现有方法需要从特定感兴趣的特定领域进行大型培训集，并且通常在通用图像上失败。在本文中，我们提出了一种不同的完成方法，该方法不需要训练集，因此可以从任何领域处理任意图像。我们的内部多元化完成（IDC）方法从最近的单图生成模型中汲取了灵感，这些模型是在单个图像的多个尺度上接受训练的，将它们调整到极端设置中，其中只有一小部分图像可供训练。我们使用用户研究和定量比较来说明IDC在几个数据集上的强度。
Image completion is widely used in photo restoration and editing applications, e.g. for object removal. Recently, there has been a surge of research on generating diverse completions for missing regions. However, existing methods require large training sets from a specific domain of interest, and often fail on general-content images. In this paper, we propose a diverse completion method that does not require a training set and can thus treat arbitrary images from any domain. Our internal diverse completion (IDC) approach draws inspiration from recent single-image generative models that are trained on multiple scales of a single image, adapting them to the extreme setting in which only a small portion of the image is available for training. We illustrate the strength of IDC on several datasets, using both user studies and quantitative comparisons.
