  Recently, Generative Adversarial Network (GAN) has been found wide
applications in style transfer, image-to-image translation and image
super-resolution. In this paper, a color-depth conditional GAN is proposed to
concurrently resolve the problems of depth super-resolution and color
super-resolution in 3D videos. Firstly, given the low-resolution depth image
and low-resolution color image, a generative network is proposed to leverage
mutual information of color image and depth image to enhance each other in
consideration of the geometry structural dependency of color-depth image in the
same scene. Secondly, three loss functions, including data loss, total
variation loss, and 8-connected gradient difference loss are introduced to
train this generative network in order to keep generated images close to the
real ones, in addition to the adversarial loss. Experimental results
demonstrate that the proposed approach produces high-quality color image and
depth image from low-quality image pair, and it is superior to several other
leading methods. Besides, we use the same neural network framework to resolve
the problem of image smoothing and edge detection at the same time.
