CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Depth map upsampling using compressive sensing based model
Dai, Longquan; Wang, Haoxing; Zhang, Xiaopeng
Source PublicationNEUROCOMPUTING
2015-04-22
Volume154Pages:325-336
SubtypeArticle
AbstractWe propose a new method to enhance the lateral resolution of depth maps with registered high-resolution color images. Inspired by the theory of compressive sensing (CS), we formulate the upsampling task as a sparse signal recovery problem that solves an underdetermined system. With a reference color image, the low-resolution depth map is converted into suitable sampling data (measurements). The signal recovery problem, defined in a constrained optimization framework, can be efficiently solved by variable splitting and alternating minimization. Experimental results demonstrate the effectiveness of our CS-based method: it competes favorably with other state-of-the-art methods with large upsampling factors and noisy depth inputs. (C) 2014 Elsevier B.V. All rights reserved.
KeywordDepth Map Compressive Sensing Upsampling
WOS HeadingsScience & Technology ; Technology
WOS KeywordUNCERTAINTY PRINCIPLES ; SIGNAL RECONSTRUCTION ; ATOMIC DECOMPOSITION ; OBJECT RECOGNITION ; PROJECTION
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000350081900033
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/8083
Collection模式识别国家重点实验室_多媒体计算与图形学
AffiliationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Dai, Longquan,Wang, Haoxing,Zhang, Xiaopeng. Depth map upsampling using compressive sensing based model[J]. NEUROCOMPUTING,2015,154:325-336.
APA Dai, Longquan,Wang, Haoxing,&Zhang, Xiaopeng.(2015).Depth map upsampling using compressive sensing based model.NEUROCOMPUTING,154,325-336.
MLA Dai, Longquan,et al."Depth map upsampling using compressive sensing based model".NEUROCOMPUTING 154(2015):325-336.
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