  Ill-posed inverse problems appear in many image processing applications, such
as deblurring and super-resolution. In recent years, solutions that are based
on deep Convolutional Neural Networks (CNNs) have shown great promise. Yet,
most of these techniques, which train CNNs using external data, are restricted
to the observation models that have been used in the training phase. A recent
alternative that does not have this drawback relies on learning the target
image using internal learning. One such prominent example is the Deep Image
Prior (DIP) technique that trains a network directly on the input image with a
least-squares loss. In this paper, we propose a new image restoration framework
that is based on minimizing a loss function that includes a "projected-version"
of the Generalized SteinUnbiased Risk Estimator (GSURE) and parameterization of
the latent image by a CNN. We demonstrate two ways to use our framework. In the
first one, where no explicit prior is used, we show that the proposed approach
outperforms other internal learning methods, such as DIP. In the second one, we
show that our GSURE-based loss leads to improved performance when used within a
plug-and-play priors scheme.
