Deep Depth Super Resolution: Learning Depth Super Resolution Using Deep Convolutional Neural Network
Depth image super resolution is an extremely challenging task due to the information loss in sub sampling. Deep convolutional neural network has 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 image. 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.
ACCV