IMAGE GUIDED DEPTH ENHANCEMENT VIA DEEP FUSION AND LOCAL LINEAR REGULARIZATION

被引:0
作者
Zhu, Jiang [1 ]
Zhang, Jing [1 ]
Cao, Yang [1 ]
Wang, Zengfu [1 ]
机构
[1] Univ Sci & Technol China, Dept Automat, Hefei, Anhui, Peoples R China
来源
2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2017年
关键词
depth enhancement; deep residual network; local linear regularization; deep feature space;
D O I
暂无
中图分类号
TB8 [摄影技术];
学科分类号
0804 ;
摘要
Depth maps captured by RGB-D cameras are often noisy and incomplete at edge regions. Most existing methods assume that there is a co-occurrence of edges in depth map and its corresponding color image, and improve the quality of depth map guided by the color image. However, when the color image is noisy or richly detailed, the high frequency artifacts will be introduced into depth map. In this paper, we propose a deep residual network based on deep fusion and local linear regularization for guided depth enhancement. The presented scheme can effectively extract the correlation between depth map and color image in the deep feature space. To reduce the difficulty of training, a specific layer of network which introduces a local linear regularization constraint on the output depth is designed. Experiments on various applications, including depth denoising, super-resolution and inpainting, demonstrate the effectiveness and reliability of our proposed approach.
引用
收藏
页码:4068 / 4072
页数:5
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