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Edge Orientation Driven Depth Super-Resolution for View Synthesis

  • Chao YaoEmail author
  • Jimin Xiao
  • Jian Jin
  • Xiaojuan BanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11903)

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

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

Notes

Acknowledgement

This research was supported in part by the National Key Research and Development Program of China (2016YFB0700502) National Natural Science Foundation of China (61873299, 61702036, 61572075).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Beijing Advanced Innovation Center for Materials Genome Engineering, School of Computer and Communication EngineeringUniversity of Science and Technology BeijingBeijingChina
  2. 2.The Department of Electrical and Electronic EngineeringXi’an Jiaotong-Liverpool UniversitySuzhouChina
  3. 3.Institute of Information ScienceBeijing Jiaotong UniversityBeijingChina

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