论文标题
通过潜在空间正则化优化图像超级分辨率的生成对抗网络
Optimizing Generative Adversarial Networks for Image Super Resolution via Latent Space Regularization
论文作者
论文摘要
自然图像可以被视为居住在嵌入更高维欧几里得空间中的歧管中。生成的对抗网络（GAN）试图学习歧管中真实图像的分布，以生成看起来真实的样本。但是，即使对于所需的地面真相目标图像，例如单图超级分辨率（SISR），现有方法的结果仍然表现出许多不愉快的文物和扭曲。我们探究了在本文中减轻这些有监督甘斯的问题的方法。我们明确应用Lipschitz的连续性条件（LCC）来正规化gan。将图像空间映射到新的最佳潜在空间的编码网络是从LCC得出的，它用于增强GAN作为耦合组件。 LCC还将在发电机损耗函数中转换为新的正规化项以实现局部不变性。 GAN与编码网络一起进行了优化，以使生成器收敛到更理想和分离的映射，该映射可以使样本更忠实于目标图像。当提出的模型应用于单个图像超级分辨率问题时，结果的表现优于最新情况。
Natural images can be regarded as residing in a manifold that is embedded in a higher dimensional Euclidean space. Generative Adversarial Networks (GANs) try to learn the distribution of the real images in the manifold to generate samples that look real. But the results of existing methods still exhibit many unpleasant artifacts and distortions even for the cases where the desired ground truth target images are available for supervised learning such as in single image super resolution (SISR). We probe for ways to alleviate these problems for supervised GANs in this paper. We explicitly apply the Lipschitz Continuity Condition (LCC) to regularize the GAN. An encoding network that maps the image space to a new optimal latent space is derived from the LCC, and it is used to augment the GAN as a coupling component. The LCC is also converted to new regularization terms in the generator loss function to enforce local invariance. The GAN is optimized together with the encoding network in an attempt to make the generator converge to a more ideal and disentangled mapping that can generate samples more faithful to the target images. When the proposed models are applied to the single image super resolution problem, the results outperform the state of the art.
