论文标题
局部二进制模式学习和空间注意
Deep Generative Model for Image Inpainting with Local Binary Pattern Learning and Spatial Attention
论文作者
论文摘要
深度学习（DL）证明了其在图像介绍领域的强大功能。基于DL的图像介入方法可以产生视觉上合理的结果，但通常会产生各种不愉快的人工制品，尤其是在边界和高质感区域。为了应对这一挑战，在这项工作中，我们通过将局部二进制模式（LBP）学习网络与实际介入网络相结合，提出了一个新的端到端两阶段（粗到细）生成模型。具体而言，使用U-NET体系结构的第一个LBP学习网络旨在准确预测缺失区域的结构信息，该网络随后指导第二张图像入内网络，以更好地填充缺失的像素。此外，通过考虑已知区域之间的一致性，还集成在图像嵌入网络中的一种改进的空间注意机制，而且还集成了生成的区域，而且在生成的区域本身之间。在包括Celeba-HQ，Places和Paris Streetview在内的公共数据集上进行的广泛实验表明，我们的模型在定量和定性上都比最先进的竞争算法产生更好的介绍结果。源代码和训练有素的模型将在https://github.com/highwaywu/imageinpainting上提供。
Deep learning (DL) has demonstrated its powerful capabilities in the field of image inpainting. The DL-based image inpainting approaches can produce visually plausible results, but often generate various unpleasant artifacts, especially in the boundary and highly textured regions. To tackle this challenge, in this work, we propose a new end-to-end, two-stage (coarse-to-fine) generative model through combining a local binary pattern (LBP) learning network with an actual inpainting network. Specifically, the first LBP learning network using U-Net architecture is designed to accurately predict the structural information of the missing region, which subsequently guides the second image inpainting network for better filling the missing pixels. Furthermore, an improved spatial attention mechanism is integrated in the image inpainting network, by considering the consistency not only between the known region with the generated one, but also within the generated region itself. Extensive experiments on public datasets including CelebA-HQ, Places and Paris StreetView demonstrate that our model generates better inpainting results than the state-of-the-art competing algorithms, both quantitatively and qualitatively. The source code and trained models will be made available at https://github.com/HighwayWu/ImageInpainting.
