@inproceedings{45baac17db7d4919b866b5186418112b,
title = "Edge Orientation Driven Depth Super-Resolution for View Synthesis",
abstract = "The limited resolution of depth images is a constraint for most of practical computer vision applications. To solve this problem, in this paper, we present a novel depth super-resolution method based on machine learning. The proposed super-resolution method incorporates an edge-orientation based depth patch clustering method, which classifies the patches into several categories based on gradient strength and directions. A linear mapping between the low resolution (LR) and high resolution (HR) patch pairs is learned for each patch category by minimizing the synthesis view distortion. Since depth maps are not viewed directly, they are used to generate the virtual views, our method takes synthesis view distortion as the optimization strategy. Experimental results show that our proposed depth super-resolution approach performs well on depth super-resolution performance and the view synthesis compared to other depth super-resolution approaches.",
keywords = "Depth-image-based rendering, Edge orientation, Linear mapping, View synthesis",
author = "Chao Yao and Jimin Xiao and Jian Jin and Xiaojuan Ban",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 10th International Conference on Image and Graphics, ICIG 2019 ; Conference date: 23-08-2019 Through 25-08-2019",
year = "2019",
doi = "10.1007/978-3-030-34113-8_10",
language = "English",
isbn = "9783030341121",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "107--121",
editor = "Yao Zhao and Chunyu Lin and Nick Barnes and Baoquan Chen and R{\"u}diger Westermann and Xiangwei Kong",
booktitle = "Image and Graphics - 10th International Conference, ICIG 2019, Proceedings, Part 3",
}