论文标题
用于介绍和空间优化联合学习的Wasserstein Gan
A Wasserstein GAN for Joint Learning of Inpainting and Spatial Optimisation
论文作者
论文摘要
图像介绍是一种修复方法，可重建缺少的图像部分。但是，经过精心选择的已知像素掩模，产生高质量的涂层也可以充当稀疏的图像表示。这个具有挑战性的空间优化问题对于诸如压缩之类的实际应用至关重要。到目前为止，基于模型的方法几乎完全对其进行了尊重。与神经网络的首次尝试似乎很有希望，但是针对特定介绍操作员量身定制的，或者需要后处理。 为了解决这个问题，我们提出了第一个用于空间介绍数据优化的生成对抗网络（GAN）。与以前的方法相反，它允许对介质发生器和相应的面罩优化网络进行联合培训。借助Wasestein距离，我们确保我们的介入结果准确地反映了自然图像的统计数据。对于常规随机模型，这可以显着改善视觉质量和速度。它还优于当前空间优化网络。
Image inpainting is a restoration method that reconstructs missing image parts. However, a carefully selected mask of known pixels that yield a high quality inpainting can also act as a sparse image representation. This challenging spatial optimisation problem is essential for practical applications such as compression. So far, it has been almost exclusively adressed by model-based approaches. First attempts with neural networks seem promising, but are tailored towards specific inpainting operators or require postprocessing. To address this issue, we propose the first generative adversarial network (GAN) for spatial inpainting data optimisation. In contrast to previous approaches, it allows joint training of an inpainting generator and a corresponding mask optimisation network. With a Wasserstein distance, we ensure that our inpainting results accurately reflect the statistics of natural images. This yields significant improvements in visual quality and speed over conventional stochastic models. It also outperforms current spatial optimisation networks.
