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	Compression Artifacts Reduction for Depth Map by Deep Intensity Guidance
	Pingping Zhang; Xu Wang; Yun Zhang; Lin Ma; Jianmin Jiang; Sam Kwong
	2017
会议日期	2017
会议地点	哈尔滨
英文摘要	In this paper, we propose a intensity guided CNN (IG-Net) model, which learns an end-to-end mapping between the intensity image and distorted depth map to the uncompressed depth map. To eliminate the undesired blocking artifacts such as discontinuities around object boundary, two branches are designed to extract the high-frequency in- formation from intensity image and depth map, respectively. Multi-scale feature fusion and enhancement layers are introduced in the main branch to strength the edge information of the restored depth map. Performance evaluation on JPEG compression artifacts shows the effectiveness and su- periority of our proposed model compared with state-of-the-art methods.
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/12641]
专题	深圳先进技术研究院_数字所
作者单位	2017
推荐引用方式 GB/T 7714	Pingping Zhang,Xu Wang,Yun Zhang,et al. Compression Artifacts Reduction for Depth Map by Deep Intensity Guidance[C]. 见:. 哈尔滨. 2017.

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