Abstract
In the recent years, there has been a significant improvement in the quality of samples produced by (deep) generative models such as variational auto-encoders and generative adversarial networks. However, the representation capabilities of these methods still do not capture the full distribution for complex classes of images, such as human faces. This deficiency has been clearly observed in previous works that use pre-trained generative models to solve imaging inverse problems. In this paper, we suggest to mitigate the limited representation capabilities of generators by making them image-adaptive and enforcing compliance of the restoration with the observations via back-projections. We empirically demonstrate the advantages of our proposed approach for image super-resolution and compressed sensing
Abstract (translated)
近年来，由变分自动编码器和生成对抗网络等生成模型产生的样本质量有了显著的提高。但是，这些方法的表示能力仍然无法捕获复杂图像类（如人脸）的完整分布。这一缺陷在以前的研究中已经被清楚地观察到，使用预先训练的生成模型来解决成像逆问题。在本文中，我们建议通过使发电机图像自适应和通过反向投影增强恢复与观测值的一致性来减轻发电机的有限表示能力。我们通过实验证明了我们提出的图像超分辨率和压缩传感方法的优点。
URL
https://arxiv.org/abs/1906.05284
