Deep intensity guidance based compression artifacts reduction for depth map
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 234-242 |
Journal / Publication | Journal of Visual Communication and Image Representation |
Volume | 57 |
Online published | 7 Nov 2018 |
Publication status | Published - Nov 2018 |
Link(s)
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
Citation Format(s)
Deep intensity guidance based compression artifacts reduction for depth map. / Wang, Xu; Zhang, Pingping; Zhang, Yun et al.
In: Journal of Visual Communication and Image Representation, Vol. 57, 11.2018, p. 234-242.
In: Journal of Visual Communication and Image Representation, Vol. 57, 11.2018, p. 234-242.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review