Color-Guided Depth Map Super Resolution Using Convolutional Neural Network

Min NI, Jianjun LEI, Runmin CONG*, Kaifu ZHENG, Bo PENG, Xiaoting FAN

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

28 Citations (Scopus)


With the development of 3-D applications, such as 3-D reconstruction and object recognition, accurate and high-quality depth map is urgently required. Recently, depth cameras have been affordable and widely used in daily life. However, the captured depth map always owns low resolution and poor quality, which limits its practical application. This paper proposes a color-guided depth map super resolution method using convolutional neural network. First, a dual-stream convolutional neural network, which integrates the color and depth information simultaneously, is proposed for depth map super resolution. Then, the optimized edge map generated by the high resolution color image and low resolution depth map is used as additional information to refine the object boundary in the depth map. Experimental results demonstrate the effectiveness of the proposed method compared with the state-of-the-art methods.

Original languageEnglish
Article number8106766
Pages (from-to)26666-26672
Number of pages7
JournalIEEE Access
Publication statusPublished - 13 Nov 2017
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • convolutional neural network
  • Depth map
  • super resolution


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