Deep intensity guidance based compression artifacts reduction for depth map

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

4 Scopus Citations
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Author(s)

  • Xu Wang
  • Pingping Zhang
  • Yun Zhang
  • Lin Ma
  • Jianmin Jiang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)234-242
Journal / PublicationJournal of Visual Communication and Image Representation
Volume57
Online published7 Nov 2018
Publication statusPublished - Nov 2018

Abstract

In this paper, we propose an deep intensity guidance based compression artifacts reduction model (denoted as DIG-Net) for depth map. The proposed DIG-Net model can learn an end-to-end mapping from the color image and distorted depth map to the uncompressed depth map. To eliminate undesired artifacts such as discontinuities around object boundary, the proposed model is with three branches, which extracts the high frequency information from color image and depth maps as priors. Based on the modified edge preserving loss function, the deep multi-scale guidance information are learned and fused in the model to make the edge of depth map sharper. Experimental results show the effectiveness and superiority of our proposed model compared with the state-of-the-art methods.

Research Area(s)

  • Compression artifacts reduction, Convolutional neural network, Depth map, JPEG compression