Edge Orientation Driven Depth Super-Resolution for View Synthesis

Chao Yao*, Jimin Xiao, Jian Jin, Xiaojuan Ban

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


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.

Original languageEnglish
Title of host publicationImage and Graphics - 10th International Conference, ICIG 2019, Proceedings, Part 3
EditorsYao Zhao, Chunyu Lin, Nick Barnes, Baoquan Chen, Rüdiger Westermann, Xiangwei Kong
Number of pages15
ISBN (Print)9783030341121
Publication statusPublished - 2019
Event10th International Conference on Image and Graphics, ICIG 2019 - Beijing, China
Duration: 23 Aug 201925 Aug 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11903 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th International Conference on Image and Graphics, ICIG 2019


  • Depth-image-based rendering
  • Edge orientation
  • Linear mapping
  • View synthesis


Dive into the research topics of 'Edge Orientation Driven Depth Super-Resolution for View Synthesis'. Together they form a unique fingerprint.

Cite this