Depth image super-resolution via multi-frame registration and deep learning

Abstract

In this paper, we develop an algorithm for depth image super-resolution from RGB-D images, which are acquired under different imaging conditions so that we can combine them to improve the image quality with precise 3D registration. We focus on how to increase the resolution and quality of depth images by combining multiple RGB-D images and using the deep learning technique. In the proposed solution, we combine multiple RGB-D images by 3D alignment from 3D feature point correspondences and apply the guided filter as the input to SRCNN to obtain the up-sampled depth images. We show depth quality improvement of the up-sampled depth maps by using the proposed algorithm over the traditional methods through experimental results on some public-domain RGB-D datasets.