Depth image super-resolution is an extremely challenging task due to the
information loss in sub-sampling. Deep convolutional neural network have been
widely applied to color image super-resolution. Quite surprisingly, this
success has not been matched to depth super-resolution. This is mainly due to
the inherent difference between color and depth images. In this paper, we
bridge up the gap and extend the success of deep convolutional neural network
to depth super-resolution. The proposed deep depth super-resolution method
learns the mapping from a low-resolution depth image to a high resolution one
in an end-to-end style. Furthermore, to better regularize the learned depth
map, we propose to exploit the depth field statistics and the local correlation
between depth image and color image. These priors are integrated in an energy
minimization formulation, where the deep neural network learns the unary term,
the depth field statistics works as global model constraint and the color-depth
correlation is utilized to enforce the local structure in depth images.
Extensive experiments on various depth super-resolution benchmark datasets show
that our method outperforms the state-of-the-art depth image super-resolution
methods with a margin.

该研究提出了一种基于深度卷积神经网络的深度图像超分辨率方法，借鉴了深度场统计学和颜色 - 深度图像之间的局部相关性等先验知识，与当前最先进的深度图像超分辨率技术相比提升了很大的表现。