CASIA OpenIR  > 模式识别国家重点实验室  > 机器人视觉
Color‐Guided Depth Map Super‐Resolution Using a Dual‐Branch Multi‐Scale Residual Network with Channel Interaction
陈睿进; 高伟
Source PublicationSensors
ISSN1424-8220
2020-03
Volume20Issue:6Pages:1560
Abstract

We designed an end‐to‐end dual‐branch residual network architecture that inputs a low-resolution (LR) depth map and a corresponding high‐resolution (HR) color image separately into the two branches, and outputs an HR depth map through a multi‐scale, channel‐wise feature extraction, interaction, and upsampling. Each branch of this network contains several residual levels at different scales, and each level comprises multiple residual groups composed of several residual blocks. A short‐skip connection in every residual block and a long‐skip connection in each residual group or level allow for low‐frequency information to be bypassed while the main network focuses on learning high‐frequency information. High‐frequency information learned by each residual block in the color image branch is input into the corresponding residual block in the depth map branch, and this kind of channel‐wise feature supplement and fusion can not only help the depth map branch to alleviate blur in details like edges, but also introduce some depth artifacts to feature maps. To avoid the above introduced artifacts, the channel interaction fuses the feature maps using weights referring to the channel attention mechanism. The parallel multi‐scale network architecture with channel interaction for feature guidance is the main contribution of our work and experiments show that our proposed method had a better performance in terms of accuracy compared with other methods.

KeywordDepth Map Super‐resolution Guidance Residual Network Channel Interaction
MOST Discipline Catalogue工学
Indexed BySCIE
Language英语
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/38536
Collection模式识别国家重点实验室_机器人视觉
Corresponding Author高伟
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
陈睿进,高伟. Color‐Guided Depth Map Super‐Resolution Using a Dual‐Branch Multi‐Scale Residual Network with Channel Interaction[J]. Sensors,2020,20(6):1560.
APA 陈睿进,&高伟.(2020).Color‐Guided Depth Map Super‐Resolution Using a Dual‐Branch Multi‐Scale Residual Network with Channel Interaction.Sensors,20(6),1560.
MLA 陈睿进,et al."Color‐Guided Depth Map Super‐Resolution Using a Dual‐Branch Multi‐Scale Residual Network with Channel Interaction".Sensors 20.6(2020):1560.
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