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A tensor-based nonlocal total variation model for multi-channel image recovery EI SCIE Scopus
期刊论文 | 2018 , 153 , 321-335 | SIGNAL PROCESSING
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Abstract :

In this paper, a new nonlocal total variation (NLTV) regularizer is proposed for solving the inverse problems in multi-channel image processing. Different from the existing nonlocal total variation regularizers that rely on the graph gradient, the proposed nonlocal total variation involves the standard image gradient and simultaneously exploits three important properties inherent in multi-channel images through a tensor nuclear norm, hence we call this proposed functional as tensor-based nonlocal total variation (TenNLTV). In specific, these three properties are the local structural image regularity, the nonlocal image self-similarity, and the image channel correlation, respectively. By fully utilizing these three properties, TenNLTV can provide a more robust measure of image variation. Then, based on the proposed regularizer TenNLTV, a novel regularization model for inverse imaging problems is presented. Moreover, an effective algorithm is designed for the proposed model, and a closed-form solution is derived for a two-order complex eigen system in our algorithm. Extensive experimental results on several inverse imaging problems demonstrate that the proposed regularizer is systematically superior over other competing local and nonlocal regularization approaches, both quantitatively and visually. (C) 2018 Elsevier B.V. All rights reserved.

Keyword :

Inverse problems Total variation Tensor Multi-channel Nonlocal regularization Image reconstruction

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GB/T 7714 Cao, Wenfei , Yao, Jing , Sun, Jian et al. A tensor-based nonlocal total variation model for multi-channel image recovery [J]. | SIGNAL PROCESSING , 2018 , 153 : 321-335 .
MLA Cao, Wenfei et al. "A tensor-based nonlocal total variation model for multi-channel image recovery" . | SIGNAL PROCESSING 153 (2018) : 321-335 .
APA Cao, Wenfei , Yao, Jing , Sun, Jian , Han, Guodong . A tensor-based nonlocal total variation model for multi-channel image recovery . | SIGNAL PROCESSING , 2018 , 153 , 321-335 .
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Proximal dehaze-net: A prior learning-based deep network for single image dehazing EI Scopus
会议论文 | 2018 , 11211 LNCS , 729-746 | 15th European Conference on Computer Vision, ECCV 2018
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Abstract :

Photos taken in hazy weather are usually covered with white masks and often lose important details. In this paper, we propose a novel deep learning approach for single image dehazing by learning dark channel and transmission priors. First, we build an energy model for dehazing using dark channel and transmission priors and design an iterative optimization algorithm using proximal operators for these two priors. Second, we unfold the iterative algorithm to be a deep network, dubbed as proximal dehaze-net, by learning the proximal operators using convolutional neural networks. Our network combines the advantages of traditional prior-based dehazing methods and deep learning methods by incorporating haze-related prior learning into deep network. Experiments show that our method achieves state-of-the-art performance for single image dehazing. © Springer Nature Switzerland AG 2018.

Keyword :

Convolutional neural network Iterative algorithm Iterative optimization algorithms Learning approach Learning methods Prior learning Single image dehazing State-of-the-art performance

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GB/T 7714 Sun, Jian . Proximal dehaze-net: A prior learning-based deep network for single image dehazing [C] . 2018 : 729-746 .
MLA Sun, Jian . "Proximal dehaze-net: A prior learning-based deep network for single image dehazing" . (2018) : 729-746 .
APA Sun, Jian . Proximal dehaze-net: A prior learning-based deep network for single image dehazing . (2018) : 729-746 .
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Location-Sensitive Sparse Representation of Deep Normal Patterns for Expression-robust 3D Face Recognition EI CPCI-S Scopus
会议论文 | 2017 , 234-242 | IEEE International Joint Conference on Biometrics (IJCB)
SCOPUS Cited Count: 1
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Abstract :

This paper presents a straight-forward yet efficient, and expression-robust 3D face recognition approach by exploring location sensitive sparse representation of deep normal patterns (DNP). In particular given raw 3D facial surfaces, we first run 3D face pre-processing pipeline, including nose tip detection, face region cropping, and pose normalization. The 3D coordinates of each normalized 3D facial surface are then projected into 2D plane to generate geometry images, from which three images of facial surface normal components are estimated. Each normal image is then fed into a pre-trained deep face net to generate deep representations of facial surface normals, i.e., deep normal patterns. Considering the importance of different facial locations, we propose a location sensitive sparse representation classifier (LS-SRC) for similarity measure among deep normal patterns associated with different 3D faces. Finally, simple score-level fusion of different normal components are used for the final decision. The proposed approach achieves significantly high performance, and reporting rank-one scores of 98.01%, 97.60%, and 96.13% on the FRGC v2.0, Bosphorus, and BU-3DFE databases when only one sample per subject is used in the gallery. These experimental results reveals that the performance of 3D face recognition would be constantly improved with the aid of training deep models from massive 2D face images, which opens the door for future directions of 3D face recognition.

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GB/T 7714 Li, Huibin , Sun, Jian , Chen, Liming . Location-Sensitive Sparse Representation of Deep Normal Patterns for Expression-robust 3D Face Recognition [C] . 2017 : 234-242 .
MLA Li, Huibin et al. "Location-Sensitive Sparse Representation of Deep Normal Patterns for Expression-robust 3D Face Recognition" . (2017) : 234-242 .
APA Li, Huibin , Sun, Jian , Chen, Liming . Location-Sensitive Sparse Representation of Deep Normal Patterns for Expression-robust 3D Face Recognition . (2017) : 234-242 .
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