Color image-guided very low-resolution depth image reconstruction

Deep learning-based image super-resolution research allows reconstruction of detailed images from low-resolution images in a short time. However, the existing depth image super-resolution reconstruction techniques focus on the reconstruction of low-resolution images with smaller downsampling multipliers, and the low-resolution images still contain richer depth information, and the superiority of super-resolution reconstruction techniques is not obvious enough. The reconstruction of depth images can be assisted by fully exploiting the rich details within the aligned 2D color images of the same scene and using the information contained internally. In this paper, we propose a network for reconstructing very low-resolution depth images with high-resolution 2D color images, mining the in-scale non-local features, cross-scale non-local features and its own priori information features of 2D color and depth images in the network. Fusing these features to greatly improve the detail accuracy of the reconstruction of very low-resolution depth images, we evaluated our method by comparing it with state-of-the-art methods comprehensively, and the experimental results demonstrate the superiority of our method.