论文阅读（一）—— Deep Image Prior，CVPR2018

Deep Image Prior是发表于CVPR2018，文章介绍了直接利用随机初始化的深度卷积（生成）网络来进行图像去噪，修补，超分辨率等图像逆向工程。

作者认为不需要从大量的图像中来学习图像的先验信息，就能做到图像修复等，也就是直接把卷积生成网络看成一般的待优化函数，直接进行梯度优化，而不是基于大量的图像学习，文章的原话是：we cast reconstruction as a conditional image generation problem and show that the only information required to solve it is contained in the single degraded input image and the handcrafted structure of the network used for reconstruction. No aspect of the network is learned from data;the weights of the network are always randomly initialized, so that the only prior information is in the structure of the network itself.

文章的方法流程如下：

1. 对于一幅待修复的图像x0
2. 随