This paper proposes a new way of regularizing an inverse problem in imaging (e.g., deblurring or inpainting) by means of a deep generative neural network. Compared to end-to-end models, such approaches seem particularly interesting since the same network can be used for many different problems and experimental conditions, as soon as the generative model is suited to the data. Previous works proposed to use a synthesis framework, where the estimation is performed on the latent vector, the solution being obtained afterwards via the decoder. Instead, we propose an analysis formulation where we directly optimize the image itself and penalize the latent vector. We illustrate the interest of such a formulation by running experiments of inpainting, deblurring and super-resolution. In many cases our technique achieves a clear improvement of the performance and seems to be more robust, in particular with respect to initialization.

翻译：本文建议采用一种新的方式,通过深厚的基因神经网络来规范成像方面的反面问题(如分解或涂漆),与端到端模型相比,这些方法似乎特别有趣,因为一旦基因模型适合数据,同一网络就可以用于许多不同的问题和实验条件。先前提议采用综合框架,对潜载体进行估计,然后通过解码器获得解决办法。相反,我们提议一种分析公式,直接优化图像本身并惩罚潜载矢量。我们通过对油漆、分解和超分辨率进行实验来说明这种配方的兴趣。在许多情况下,我们的技术能够明显地改进性能,而且似乎更加有力,特别是在初始化方面。