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.

翻译：近年来，变分自编码器和生成对抗网络等（深度）生成模型生成的样本质量有了显著提升。然而，这些方法的表示能力仍未能完全捕捉复杂图像类别（如人脸）的完整分布。先前利用预训练生成模型解决成像逆问题的工作中已明显观察到这一缺陷。本文提出通过使生成器具备图像自适应性，并借助反向投影强制复原结果与观测数据的一致性，从而缓解生成器表示能力受限的问题。我们通过实验验证了所提方法在图像超分辨率和压缩感知任务中的优势。