Jiaming Wang

Wuhan Institute of Technology

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Tao Lü

Wuhan Institute of Technology

Zhenfeng Shao

State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

Xiao Huang

Emory University

Ruiqian Zhang

Huanggang Normal University

Yanduo Zhang

Hubei University of Arts and Science

Jun Du

University of Science and Technology of China

Yu Wang

State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

Zhongyuan Wang

Wuhan University

Jianshu Zhang

Nanjing University of Science and Technology

Jiayi Ma

Wuhan University

Cooperative Institutions

Wuhan University

Wuhan Institute of Technology

Chinese Academy of Sciences

Huazhong University of Science and Technology

State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

Tsinghua University

University of Chinese Academy of Sciences

University of Science and Technology of China

Zhejiang University

Harbin Institute of Technology

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Data-driven Face Hallucination by Inverse Degradation Neural Network

Ruobo Xu Jiaming Wang Tao Lü

Face hallucination refers to the technology that inferring its potential corresponding high-resolution (HR) image from the input low-resolution (LR) facial image. At present, most face hallucination algorithms improve reconstruction performance by optimizing models. However, the common approach will out of operation when meeting more complex problem, etc, the input image contains degraded pixels (noise), their reconstruction performance will drop sharply. In order to solve the problem, we propose an inverse degradation neural network (IDNN), which can mine the essential features of the images under data-driven. In this network, we design different network structures in different task stages. Firstly, the more accurated face structure is generated by the denoising network in the LR space. But the details from the face image is lacked in this stage. In order to further enhance the face image details, we utilize the reconstruction network to restore the missing details. The experimental results on FEI face database show that IDNN outperforms some state-of-the-art approaches in subjective and objective measures.

Face hallucination

10.1145/3366715.3366744

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The Most Unfavorable Loop and the Thermodynamic Stability

Tianyi Zhao Jiaming Wang Yi‐Ting Wang

10.1007/978-981-96-0377-0_4

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Application of Deep Learning in Ground Penetrating Radar Image Recognition

Jie Yang Song Fubin Pengli Zhang Jiaming Wang Chao Cui

In the traditional ground-penetrating radar image recognition methods, the recognition accuracy is not high, the recognition of complex targets is difficult, the recognition process is cumbersome, and the structural characteristics reflected by the measured data cannot be accurately judged. In this paper, a Faster R-CNN target detection algorithm in deep learning is applied to GPR image recognition. Firstly, based on the three-phase discrete random medium model, GPRmax software is used for forwarding simulation, and GPR images of different earth-rock dam disease types are obtained by changing the position, shape, and range of different disease types, and GPR image data sets are expanded using horizontal flipping, random erasing, scale transformation and adding random noise. Under the framework of TensorFlow, based on the Faster R-CNN target detection algorithm, the training and testing of the earth-rock dam disease target detection model are completed. The recognition performance on the test set is analyzed from three indexes of recall rate, precision rate and average precision rate, and evaluation indexes such as accuracy-recall rate curve. The results show that the average accuracy of the constructed target detection model for earth-rock dam disease recognition is over 90%. Among them, the recognition effect of concentrated leakage, scattered leaching, and cracks is the best. Finally, the effectiveness of the target detection model established in this paper is verified by the measured data of an abandoned slag slope.

Ground-Penetrating Radar

10.1109/caibda53561.2021.00011

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Unsupervised Remote Sensing Super-Resolution via Migration Image Prior

arXiv (Cornell University) (2021)

Jiaming Wang Zhenfeng Shao Tao Lü Xiao Huang Ruiqian Zhang

Recently, satellites with high temporal resolution have fostered wide attention in various practical applications. Due to limitations of bandwidth and hardware cost, however, the spatial resolution of such satellites is considerably low, largely limiting their potentials in scenarios that require spatially explicit information. To improve image resolution, numerous approaches based on training low-high resolution pairs have been proposed to address the super-resolution (SR) task. Despite their success, however, low/high spatial resolution pairs are usually difficult to obtain in satellites with a high temporal resolution, making such approaches in SR impractical to use. In this paper, we proposed a new unsupervised learning framework, called "MIP", which achieves SR tasks without low/high resolution image pairs. First, random noise maps are fed into a designed generative adversarial network (GAN) for reconstruction. Then, the proposed method converts the reference image to latent space as the migration image prior. Finally, we update the input noise via an implicit method, and further transfer the texture and structured information from the reference image. Extensive experimental results on the Draper dataset show that MIP achieves significant improvements over state-of-the-art methods both quantitatively and qualitatively. The proposed MIP is open-sourced at http://github.com/jiaming-wang/MIP.

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Rethinking the Role of Panchromatic Images in Pan-sharpening

IEEE Transactions on Multimedia (2025)

Jiaming Wang Xitong Chen Xiao Huang Ruiqian Zhang Yu Wang

Panchromatic film

Sharpening

10.1109/tmm.2025.3535309

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A Real-Time Lightweight Detection Algorithm for Deck Crew and the Use of Fishing Nets based on Improved YOLOv5s Network

Jiaming Wang Yin Xiangbo Guodong Li

A real-time monitoring system for the operational status of fishing vessels is an essential element for the modernization of the fishing industry. The operational status of fishing vessels can be identified by using onboard cameras to detect the deck crew and the use of fishing nets. Due to the typically limited processing capacity of shipboard equipment and the significant memory consumption of detection models, however, general target detection models are unable to perform real-time image detection to identify the operational status of fishing vessels. In this paper, we propose a lightweight real-time deck crew and the use of fishing nets detection method, YOLOv5s-SGC. It is based on the YOLOv5s model, which uses surveillance cameras to obtain video of fishing vessels operating at sea and enhances the dataset. YOLOv5s-SGC replaces YOLOv5s’s backbone and the ordinary convolutional blocks in the feature fusion network with ShufflNetV2 and Ghost module.

10.20944/preprints202305.1115.v1

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MRD: A Memory Relation Decoder for Online Handwritten Mathematical Expression Recognition

Lecture notes in computer science (2021)

Jiaming Wang Qing Wang Jun Du Jianshu Zhang Bin Wang

Symbol (formal)

Benchmark (surveying)

Expression (computer science)

10.1007/978-3-030-86334-0_3

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A Real-Time Lightweight Detection Algorithm for Deck Crew and the Use of Fishing Nets Based on Improved YOLOv5s Network

Fishes (2023)

Jiaming Wang Xiangbo Yin Guodong Li

A real-time monitoring system for the operational status of fishing vessels is an essential element for the modernization of the fishing industry. The operational status of fishing vessels can be identified by using onboard cameras to detect the deck crew and the use of fishing nets. Due to the typically limited processing capacity of shipboard equipment and the significant memory consumption of detection models, general target detection models are unable to perform real-time image detection to identify the operational status of fishing vessels. In this paper, we propose a lightweight real-time deck crew and the use of a fishing net detection method, YOLOv5s-SGC. It is based on the YOLOv5s model, which uses surveillance cameras to obtain video of fishing vessels operating at sea and enhances the dataset. YOLOv5s-SGC replaces the backbone of YOLOv5s with ShuffleNetV2, replaces the feature fusion network with a modified Generalized-FPN, and adds the CBAM attention module in front of the detection head.

10.3390/fishes8070376

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Cross-view Aggregation Network For Stereo Image Super-Resolution

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2024)

Zhitao Chen Tao Lü Kanghui Zhao Bolin Zhu Zhen Li

10.1109/cvprw63382.2024.00647

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Fingertip detection based on Freeman chain code analysis

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) (2022)

Yihong Wang Jiaming Wang

In order to meet the requirements of real-time fingertip positioning and detection in some scenes, a fingertip detection method based on Freeman chain code analysis is proposed in this paper. Firstly, the image collected by the camera needs to be preprocessed. The edge information of the palm is obtained by median filtering, analyzing the skin color space, binarization and morphological processing. The Freeman chain code of the hand contour is obtained by contour tracking calculation. After the convex points of the chain code of the hand contour are repaired, the matching points are analyzed according to the chain code difference, so as to realize the function of fingertip detection. The test shows that the fingertip detection based on Freeman chain code can detect the fingertip position quickly and accurately, and has good detection performance.

Chain code

Code (set theory)

Corner detection

Position (finance)

10.1109/aemcse55572.2022.00132

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