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|a 10.1109/TIP.2021.3131048
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|a eng
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| 100 |
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|a Cheng, Chunbo
|e verfasserin
|4 aut
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|a Two-Branch Deconvolutional Network With Application in Stereo Matching
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|c 2022
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 10.12.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Deconvolutional networks have attracted extensive attention and have been successfully applied in the field of computer vision. In this paper we propose a novel two-branch deconvolutional network (TBDN) that can improve the performance of conventional deconvolutional networks and reduce the computational complexity. A feasible iterative algorithm is designed to solve the optimization problem for the TBDN model, and a theoretical analysis of the convergence and computational complexity for the algorithm is also provided. The application of the TBDN in stereo matching is presented by constructing a disparity estimation network. Extensive experimental results on four commonly used datasets demonstrate the efficiency and effectiveness of the proposed TBDN
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|a Journal Article
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|a Li, Hong
|e verfasserin
|4 aut
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| 700 |
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|a Zhang, Liming
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 31(2022) vom: 06., Seite 327-340
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|g volume:31
|g year:2022
|g day:06
|g pages:327-340
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|u http://dx.doi.org/10.1109/TIP.2021.3131048
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