Abstract-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 lowresolution depth image to a high resolution one in an end-toend 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 colordepth 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 stateof-the-art depth image super-resolution methods with a margin.
For example, Song et al. REF used deep convolutional neural network to learn the end-toend mapping from LR depth image to HR depth image, and then further process the learned HR depth images.
Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network
ArXiv
ArXiv
Computer Science