Globally and Locally Consistent Image Completion
Globally and Locally Consistent Image Completion
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DOI：
10.1145/3072959.3073659
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发表时间：
2017-07-01
影响因子：
6.2
通讯作者：
Ishikawa, Hiroshi
中科院分区：
文献类型：
--
作者：
Iizuka, Satoshi;Simo-Serra, Edgar;Ishikawa, Hiroshi
我们提出了一种新的方法，图像完成，结果在本地和全球一致的图像。使用全卷积神经网络，我们可以通过填充任何形状的缺失区域来完成任意分辨率的图像。为了训练这个图像完成网络保持一致，我们使用全局和局部上下文判别器，这些判别器经过训练，可以区分真实的图像和完成的图像。全局鉴别器查看整个图像以评估其作为一个整体是否连贯，而局部鉴别器仅查看以完成区域为中心的一个小区域，以确保生成的补丁的局部一致性。然后训练图像补全网络来欺骗两个上下文匹配网络，这要求它生成的图像在整体一致性和细节方面与真实的图像无法区分。我们表明，我们的方法可以用来完成各种各样的场景。此外，与基于补丁的方法（如PatchMatch）相比，我们的方法可以生成图像中其他地方没有出现的片段，这使我们能够自然地完成具有熟悉和高度特定结构的对象的图像，如面部。
We present a novel approach for image completion that results in images that are both locally and globally consistent. With a fully-convolutional neural network, we can complete images of arbitrary resolutions by filling in missing regions of any shape. To train this image completion network to be consistent, we use global and local context discriminators that are trained to distinguish real images from completed ones. The global discriminator looks at the entire image to assess if it is coherent as a whole, while the local discriminator looks only at a small area centered at the completed region to ensure the local consistency of the generated patches. The image completion network is then trained to fool the both context discriminator networks, which requires it to generate images that are indistinguishable from real ones with regard to overall consistency as well as in details. We show that our approach can be used to complete a wide variety of scenes. Furthermore, in contrast with the patch-based approaches such as PatchMatch, our approach can generate fragments that do not appear elsewhere in the image, which allows us to naturally complete the images of objects with familiar and highly specific structures, such as faces.