Dr. Jun Wang Profile

Dr. Jun Wang

at Northwest Univ

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Author

Publications (7)

Proceedings Article | 9 August 2023 Presentation + Paper

Intelligent labeling of murals with fragmentation disease based on gradient trainable Gabor and U-Net

Jinye Peng, Yang Yu, Qunxi Zhang, Yuheng Li, Yiming Li, Jing Ji, Lvzheng Zhang, Kai Yu, Jun Wang

Proceedings Volume 12620, 1262005 (2023) https://doi.org/10.1117/12.2673755

KEYWORDS: Image segmentation, Tunable filters, Feature extraction, Image processing

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Proceedings Article | 9 August 2023 Presentation + Paper

Multi-scale convolutional autoencoder network-based implicit information mining of cultural relics

Kai Yu, Zhichao Li, Yi Duan, Lin Wang, Jun Wang, Jinye Peng

Proceedings Volume 12620, 1262003 (2023) https://doi.org/10.1117/12.2673753

KEYWORDS: Data hiding, Mining, Convolution, Machine learning, Hyperspectral imaging, Feature extraction, Data modeling, Principal component analysis, Data processing, Image fusion

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Implicit information exploration techniques are of great importance for the restoration and conservation of cultural relics. At present, the hyperspectral image analysis technique is one of the main methods to extract hidden information, which mainly contains two analysis methods such as principal component analysis (PCA) and minimum noise fraction rotation (MNF), both of which have achieved certain information extraction effects. In recent years, with the development of artificial intelligence, deep learning, and other technologies, nonlinear methods such as neural networks are expected to further improve the effect of implicit information mining. Therefore, this paper is oriented to the problem of extracting hidden information from pottery artifacts and tries to study and explore the hidden information mining method based on deep neural networks, expecting to obtain more stable and richer hidden information. In this paper, an auto-encoder-based implied information mining method is proposed first, and the auto-encoder (AE) framework achieves good performance in feature learning by automatically learning low-dimensional embedding and reconstructing data. However, during the experiments, it is found that some important detailed information (e.g., implicit information) is often lost in the reconstruction process because the traditional autoencoder network only focuses more on the pixel-level reconstruction loss and ignores the overall distribution. Therefore, this paper further proposes a multi-scale convolutional autoencoder network (MSCAE). It constructs a multi-scale convolutional module based on the traditional AE and designs a cyclic consistency loss in addition to the reconstruction loss, to reduce the loss of detailed information in the reconstruction process and improve the implicit information mining effect. In the experiments, we find that the proposed method can achieve effective implied information mining by extracting implied information from cocoon-shaped pots, and its visual effect has been improved compared with the traditional AE network.

SPIE Journal Paper | 11 October 2021

Attention deep and shallow network union HR optical flow compensation for video super-resolution

Xuan Zhu, Yuying Jin, Lin Wang, Jun Wang, Zhixiang Lan, Peirong Chen, Yifei Sun

OE, Vol. 60, Issue 10, 103102, (October 2021) https://doi.org/10.1117/12.10.1117/1.OE.60.10.103102

KEYWORDS: Video, Optical flow, Lawrencium, Super resolution, Optical networks, Video acceleration, Optical engineering, Visualization, Zoom lenses, Convolution

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SPIE Journal Paper | 14 September 2021

Temporal–spatial feature compensation combines with multi-feature discriminators for video super-resolution perceptual quality improvement

Xuan Zhu, Xin Liu, Lin Wang, Zhenpeng Guo, Jun Wang, Rongzhi Wang, Yifei Sun

JEI, Vol. 30, Issue 05, 053005, (September 2021) https://doi.org/10.1117/12.10.1117/1.JEI.30.5.053005

KEYWORDS: Video, Lawrencium, Video surveillance, Optical flow, Visualization, Gallium nitride, Super resolution, Convolution, Surveillance, Distortion

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Proceedings Article | 8 July 2021 Presentation + Paper

Intelligent labeling of areas of wall painting with paint loss disease based on multi-scale detail injection U-Net

Kai Yu, Yuheng Li, Jing Yan, Ruiheng Xie, Erlei Zhang, Cheng Liu, Jun Wang

Proceedings Volume 11784, 1178409 (2021) https://doi.org/10.1117/12.2593813

KEYWORDS: Orthophoto maps, Machine learning, Image segmentation, Image processing, Human-computer interaction, Artificial intelligence

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Proceedings Article | 8 July 2021 Presentation + Paper

Deep learning for the extraction of sketches from spectral images of historical paintings

Qunxi Zhang, Shan Cui, Lu Liu, Jiaxin Wang, Jun Wang, Erlei Zhang, Jinye Peng, Sotiria Kogou, Florence Liggins, Haida Liang

Proceedings Volume 11784, 1178407 (2021) https://doi.org/10.1117/12.2593680

KEYWORDS: Edge detection, Convolution, Imaging spectroscopy, Image segmentation, Data modeling, Near infrared, Principal component analysis, Independent component analysis, Computer programming, Reflectivity

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The sketches of painted cultural objects can be the most indicative of the style of paintings. Extraction of the sketches is an integral process used by conservators and art historians for documentation and for artists to learn historical painting styles through copying and painting. However, at present, sketch extraction is mainly manually drawn, which is not only time-consuming, but also subjective and dependent on experience. Therefore, both accuracy and efficiency need to be improved. In recent years, with the development of machine learning, a series of extraction methods based on edge detection have emerged. However, most of the existing methods can only perform successful extraction if the sketches are well preserved , but for the data with faded sketches or severe conservation issues, the extraction methods need to be improved. It is beneficial to extract the bands that accentuate the sketches while suppressing the effects of the degraded areas and the overlapping paints. We propose a sketch extraction method based on hyperspectral image and deep learning. Firstly, the hyperspectral image data is collected and the bands sensitive to the sketches are extracted by a prior knowledge of the sketches (e.g. near infrared bands will be chosen if the sketches are made of carbon ink), and a dataset including a large number of existing natural images is used to pre-train the bi-directional cascade network (BDCN). The network parameters in the model are then fine-tuned by using the images of painted cultural objects drawn by experts, so as to solve the problem of insufficient sketch dataset of painted cultural objects and enhance the generalization ability of the model. Finally, the U-net network is used to further suppress the noise, i.e. unwanted information, and make the sketch clearer. The experimental results show that the proposed method can not only effectively extract sketch from ideal data, but also extract clear sketches from data with faded sketches and even with noise interference. It is superior to the other six advanced based on edge detection methods in visual and objective comparison, and has a good application prospect. The proposed deep learning method is also compared with an unsupervised clustering method using Self-Organising Map (SOM) which is a ‘shallow learning’ method where pixels of similar spectra are grouped into clusters without the need for data labeling by experts.

Proceedings Article | 12 July 2019 Presentation + Paper

A method for the analysis of spectral imaging data from Tang tomb murals

Qunxi Zhang, Jun Wang, Zhenrong Sun, Jinye Peng, Haida Liang

Proceedings Volume 11058, 110581J (2019) https://doi.org/10.1117/12.2527378

KEYWORDS: Imaging spectroscopy, Imaging systems, Analytical research, Hyperspectral imaging, Spectral data processing

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