Deep neural networks have been very successful in image estimation
applications such as compressive-sensing and image restoration, as a means to
estimate images from partial, blurry, or otherwise degraded measurements. These
networks are trained on a large number of corresponding pairs of measurements
and ground-truth images, and thus implicitly learn to exploit domain-specific
image statistics. But unlike measurement data, it is often expensive or
impractical to collect a large training set of ground-truth images in many
application settings. In this paper, we introduce an unsupervised framework for
training image estimation networks, from a training set that contains only
measurements---with two varied measurements per image---but no ground-truth for
the full images desired as output. We demonstrate that our framework can be
applied for both regular and blind image estimation tasks, where in the latter
case parameters of the measurement model (e.g., the blur kernel) are unknown:
during inference, and potentially, also during training. We evaluate our method
for training networks for compressive-sensing and blind deconvolution,
considering both non-blind and blind training for the latter. Our unsupervised
framework yields models that are nearly as accurate as those from fully
supervised training, despite not having access to any ground-truth images.

本文提出了一种无监督框架用于训练图像估计网络，针对仅含测量值而没有地面真实图像的训练集，可以应用在压缩感知和盲复原等图像估计任务中。该无监督框架可以用于常规和盲估计任务，而且在考虑到非盲和盲训练的情况下，在压缩感知和盲复原中训练出的模型近似与全监督则没有太大的差距。