Simultaneous color-depth super-resolution with conditional generative adversarial networks

Lijun Zhao, Huihui Bai, Jie Liang, Bing Zeng, Anhong Wang, Yao Zhao

Abstract: Highlights•In consideration of the geometric structural similarity of color-depth images, a generative network is proposed to leverage mutual information of the color image and depth image to enhance each other.•Three loss functions, including data loss, total variation loss, and 8-connected gradient difference loss are introduced to train this generative network to make generated images close to the real ones.•We use our framework to resolve the problems of simultaneous image smoothing and edge detection, as well as HR-color-image-guided depth super-resolution to show the effectiveness and universality of the proposed method.

External IDs:doi:10.1016/j.patcog.2018.11.028