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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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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
WoS CC Cited Count: 1 SCOPUS Cited Count: 2
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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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求解反问题的一个新方法:模型求解与范例学习结合 CSCD
期刊论文 | 2017 , (10) | 中国科学(数学)
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Keyword :

深度学习 反问题 范例学习

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GB/T 7714 徐宗本 , 杨燕 , 孙剑 . 求解反问题的一个新方法:模型求解与范例学习结合 [J]. | 中国科学(数学) , 2017 , (10) .
MLA 徐宗本 et al. "求解反问题的一个新方法:模型求解与范例学习结合" . | 中国科学(数学) 10 (2017) .
APA 徐宗本 , 杨燕 , 孙剑 . 求解反问题的一个新方法:模型求解与范例学习结合 . | 中国科学(数学) , 2017 , (10) .
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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)
WoS CC Cited Count: 4 SCOPUS Cited Count: 4
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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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图像多标签学习综述
期刊论文 | 2011 , (6) , 490-496 | 云南民族大学学报(自然科学版)
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Abstract :

图像语义标签的自动标注技术是当前信息检索领域的热点问题.图像标注本质上是一个机器学习问题,即如何根据图像的视觉内容推导图像的语义标签.综述了图像标注的发展和现状,并对目前比较流行的图像标注算法进行深入的讨论和比较研究.最后提出一种目前较新且值得深入研究的基于稀疏编码的图像标注算法.

Keyword :

稀疏编码 多标签 图像特征 图像标注 语义鸿沟

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GB/T 7714 黄焱 , 孙剑 , 谷雨 . 图像多标签学习综述 [J]. | 云南民族大学学报(自然科学版) , 2011 , (6) : 490-496 .
MLA 黄焱 et al. "图像多标签学习综述" . | 云南民族大学学报(自然科学版) 6 (2011) : 490-496 .
APA 黄焱 , 孙剑 , 谷雨 . 图像多标签学习综述 . | 云南民族大学学报(自然科学版) , 2011 , (6) , 490-496 .
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彩色图像分割中基于图上半监督学习算法研究 CSCD PKU
期刊论文 | 2011 , (2) , 11-14,20 | 西安交通大学学报
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Abstract :

提出一种新的基于图上半监督学习的彩色图像前景/背景分割模型与算法.该算法的目的是利用人工标定的部分像素点分割信息以实现对整幅图像的分割.通过结合像素点颜色特征和像素点颜色与前景/背景颜色的相似性特征,构造了新的图节点之间的双高斯权重函数,并对此提出自适应的参数选择策略与彩色图像半监督分割的能量模型,通过优化该能量模型将已知像素点的标号信息扩散到未知像素点.实验结果表明,所提出的新算法较已有算法具有更高的分割精度,因此具有重要的应用价值.

Keyword :

双高斯模型 颜色相似性特征 交互式图像分割 图上半监督

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GB/T 7714 陈蓉 , 孙剑 , 徐宗本 . 彩色图像分割中基于图上半监督学习算法研究 [J]. | 西安交通大学学报 , 2011 , (2) : 11-14,20 .
MLA 陈蓉 et al. "彩色图像分割中基于图上半监督学习算法研究" . | 西安交通大学学报 2 (2011) : 11-14,20 .
APA 陈蓉 , 孙剑 , 徐宗本 . 彩色图像分割中基于图上半监督学习算法研究 . | 西安交通大学学报 , 2011 , (2) , 11-14,20 .
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计算机视觉中的尺度空间方法 CSCD PKU
期刊论文 | 2005 , (6) , 951-962 | 工程数学学报
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Abstract :

近年来,偏微分方程、变分法和数学形态学等现代数学方法被广泛应用于计算机视觉领域,尺度空间方法作为这些方法的统一框架,已逐渐成为国际上计算机视觉和图像处理领域研究的热点.本文综述尺度空间方法的基本思想、理论基础、视觉处理能力及实现方法,然后提出尺度空间方法理论和应用值得研究的若干问题.

Keyword :

尺度空间方法 数学形态学 偏微分方程 计算机视觉

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GB/T 7714 孙剑 , 徐宗本 . 计算机视觉中的尺度空间方法 [J]. | 工程数学学报 , 2005 , (6) : 951-962 .
MLA 孙剑 et al. "计算机视觉中的尺度空间方法" . | 工程数学学报 6 (2005) : 951-962 .
APA 孙剑 , 徐宗本 . 计算机视觉中的尺度空间方法 . | 工程数学学报 , 2005 , (6) , 951-962 .
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