.png){kind=icon}
返回
Edge-Guided Single Depth Image Super Resolution
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2015.2501749.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recently, consumer depth cameras have gained significant popularity due to their affordable cost. However, the limited resolution and the quality of the depth map generated by these cameras are still problematic for several applications. In this paper, a novel framework for the single depth image superresolution is proposed. In our framework, the upscaling of a single depth image is guided by a high-resolution edge map, which is constructed from the edges of the low-resolution depth image through a Markov random field optimization in a patch synthesis based manner. We also explore the self-similarity of patches during the edge construction stage, when limited training data are available. With the guidance of the high-resolution edge map, we propose upsampling the high-resolution depth image through a modified joint bilateral filter. The edge-based guidance not only helps avoiding artifacts introduced by direct texture prediction, but also reduces jagged artifacts and preserves the sharp edges. Experimental results demonstrate the effectiveness of our method both qualitatively and quantitatively compared with the state-of-the-art methods.
Keyword:
Single depth image
super resolution
edge-guided
joint bilateral up-sampling
Markov random field
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
机构
引用论文
Extraction of cholesterol and other lipids from dehydrated beef using supercritical carbon dioxide
.png){kind=icon}
PREAI
分享收藏
A Comparative Assessment of Runoff Nitrogen from Turf, Forest, Meadow, and Mixed Landuse Watersheds
PREAI
分享收藏
查看更多内容