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231225s2019 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2019.2961672
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|a eng
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| 100 |
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|a Gu, Shuhang
|e verfasserin
|4 aut
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| 245 |
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|a Learned Dynamic Guidance for Depth Image Reconstruction
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|c 2019
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|a Text
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|a ƒaComputermedien
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|a Date Revised 27.02.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a The depth images acquired by consumer depth sensors (e.g., Kinect and ToF) usually are of low resolution and insufficient quality. One natural solution is to incorporate a high resolution RGB camera and exploit the statistical correlation of its data and depth. In recent years, both optimization-based and learning-based approaches have been proposed to deal with the guided depth reconstruction problems. In this paper, we introduce a weighted analysis sparse representation (WASR) model for guided depth image enhancement, which can be considered a generalized formulation of a wide range of previous optimization-based models. We unfold the optimization by the WASR model and conduct guided depth reconstruction with dynamically changed stage-wise operations. Such a guidance strategy enables us to dynamically adjust the stage-wise operations that update the depth image, thus improving the reconstruction quality and speed. To learn the stage-wise operations in a task-driven manner, we propose two parameterizations and their corresponding methods: dynamic guidance with Gaussian RBF nonlinearity parameterization (DG-RBF) and dynamic guidance with CNN nonlinearity parameterization (DG-CNN). The network structures of the proposed DG-RBF and DG-CNN methods are designed with the the objective function of our WASR model in mind and the optimal network parameters are learned from paired training data. Such optimization-inspired network architectures enable our models to leverage the previous expertise as well as take benefit from training data. The effectiveness is validated for guided depth image super-resolution and for realistic depth image reconstruction tasks using standard benchmarks. Our DG-RBF and DG-CNN methods achieve the best quantitative results (RMSE) and better visual quality than the state-of-the-art approaches at the time of writing. The code is available at https://github.com/ShuhangGu/GuidedDepthSR
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|a Journal Article
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1 |
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|a Guo, Shi
|e verfasserin
|4 aut
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| 700 |
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|a Zuo, Wangmeng
|e verfasserin
|4 aut
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| 700 |
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|a Chen, Yunjin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Timofte, Radu
|e verfasserin
|4 aut
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| 700 |
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|a Van Gool, Luc
|e verfasserin
|4 aut
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| 700 |
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|a Zhang, Lei
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g (2019) vom: 23. Dez.
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g year:2019
|g day:23
|g month:12
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|u http://dx.doi.org/10.1109/TPAMI.2019.2961672
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