论文标题
发电机金字塔用于高分辨率图像介绍
Generator Pyramid for High-Resolution Image Inpainting
论文作者
论文摘要
用大孔的高分辨率图像介绍了现有的基于深度学习的图像介入方法的挑战。我们提出了一个新颖的框架 - 用于高分辨率图像介入任务的金字塔填充，该任务明确地删除了内容完成和纹理综合。金字塔菲尔试图在低分辨率图像中完成未知区域的内容，并逐渐综合了高分辨率图像中未知区域的纹理。因此，我们的模型由一个完全卷积的gan的金字塔组成，其中内容gan负责在最低分辨率的掩盖图像中完成内容，并且每个纹理gan均负责在高分辨率图像中合成纹理。由于完成内容和综合纹理要求与发电机不同的能力，因此我们为内容gan和纹理gan自定义不同的体系结构。在多个数据集上进行的实验，包括Celeba-HQ，Place2和具有不同分辨率的新自然风景数据集（NSHQ），这表明，与先进的方法相比，金字塔菲尔会产生更高质量的入学结果。为了更好地评估高分辨率图像覆盖方法，我们将发布具有高分辨率1920 $ \ times $ 1080的NSHQ，高质量的自然风景图像。
Inpainting high-resolution images with large holes challenges existing deep learning based image inpainting methods. We present a novel framework -- PyramidFill for high-resolution image inpainting task, which explicitly disentangles content completion and texture synthesis. PyramidFill attempts to complete the content of unknown regions in a lower-resolution image, and synthesis the textures of unknown regions in a higher-resolution image, progressively. Thus, our model consists of a pyramid of fully convolutional GANs, wherein the content GAN is responsible for completing contents in the lowest-resolution masked image, and each texture GAN is responsible for synthesizing textures in a higher-resolution image. Since completing contents and synthesising textures demand different abilities from generators, we customize different architectures for the content GAN and texture GAN. Experiments on multiple datasets including CelebA-HQ, Places2 and a new natural scenery dataset (NSHQ) with different resolutions demonstrate that PyramidFill generates higher-quality inpainting results than the state-of-the-art methods. To better assess high-resolution image inpainting methods, we will release NSHQ, high-quality natural scenery images with high-resolution 1920$\times$1080.
