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A Novel Multi-Stream Fusion Network for Underwater image Enhancement

China communications 2024年第2期21卷 166-182页

作者： Guijin Tang Lian Duan Haitao Zhao Feng Liu Jiangsu Key Laboratory of Image Processing and Image Communication Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and TelecommunicationsNanjing 210003China

Due to the selective absorption of light and the existence of a large number of floating media in sea water, underwater images often suffer from color casts and detail blurs. It is therefore necessary to perform color correction and detail restoration. However,the existing enhancement algorithms cannot achieve the desired results. In order to solve the above problems, this paper proposes a multi-stream feature fusion network. First, an underwater image is preprocessed to obtain potential information from the illumination stream, color stream and structure stream by histogram equalization with contrast limitation, gamma correction and white balance, respectively. Next, these three streams and the original raw stream are sent to the residual blocks to extract the features. The features will be subsequently fused. It can enhance feature representation in underwater images. In the meantime, a composite loss function including three terms is used to ensure the quality of the enhanced image from the three aspects of color balance, structure preservation and image smoothness. Therefore, the enhanced image is more in line with human visual ***, the effectiveness of the proposed method is verified by comparison experiments with many stateof-the-art underwater image enhancement algorithms. Experimental results show that the proposed method provides superior results over them in terms of MSE,PSNR, SSIM, UIQM and UCIQE, and the enhanced images are more similar to their ground truth images.

关键词： image enhancement multi-stream fusion underwater image

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Low-light image enhancement algorithm using a residual network with semantic information

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The Journal of China Universities of posts and telecommunications 2022年第2期29卷 52-62,84页

作者： Duan Lian Tang Guijin Jiangsu Key Laboratory of Image Processing and Image Communication Nanjing University of Posts and TelecommunicationsNanjing 210003China

Aiming to solve the poor performance of low illumination enhancement algorithms on uneven illumination images,a low-light image enhancement(LIME)algorithm based on a residual network was *** algorithm constructs a deep network that uses residual modules to extract image feature information and semantic modules to extract image semantic information from different ***,a composite loss function was also designed for the process of low illumination image enhancement,which dynamically evaluated the loss of an enhanced image from three factors of color,structure,and *** ensures that the model can correctly enhance the image features according to the image semantics,so that the enhancement results are more in line with the human visual *** results show that compared with the state-of-the-art algorithms,the semantic-driven residual low-light network(SRLLN)can effectively improve the quality of low illumination images,and achieve better subjective and objective evaluation indexes on different types of images.

关键词： image enhancement convolutional neural network(CNN) residual learning image semantics

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Learning Dual-Domain Calibration and Distance-Driven Correlation Filter:A Probabilistic Perspective for UAV Tracking

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Computers, Materials & Continua 2023年第12期77卷 3741-3764页

作者： Taiyu Yan Yuxin Cao Guoxia Xu Xiaoran Zhao Hu Zhu Lizhen Deng Jiangsu Province Key Lab on Image Processing and Image Communication Nanjing University of Posts and TelecommunicationsNanjingChina School of Computer Science Qufu Normal UniversityQufuChina National Engineering Research Center of Communication and Network Technology Nanjing University of Posts and TelecommunicationsNanjingChina

Unmanned Aerial Vehicle(UAV)tracking has been possible because of the growth of intelligent information technology in smart cities,making it simple to gather data at any time by dynamically monitoring events,people,the environment,and other aspects in the *** traditional filter creates a model to address the boundary effect and time filter degradation issues in UAV tracking *** these methods ignore the loss of data integrity terms since they are overly dependent on numerous explicit previous regularization *** light of the aforementioned issues,this work suggests a dual-domain Jensen-Shannon divergence correlation filter(DJSCF)model address the probability-based distance measuring issue in the event of filter *** two-domain weighting matrix and JS divergence constraint are combined to lessen the impact of sample imbalance and *** new tracking models that are based on the perspectives of the actual probability filter distribution and observation probability filter distribution are proposed to translate the statistical distance in the online tracking model into response *** model is roughly transformed into a linear equality constraint issue in the iterative solution,which is then solved by the alternate direction multiplier method(ADMM).The usefulness and superiority of the suggested strategy have been shown by a vast number of experimental findings.

关键词： Dual-domain weighting distance measure correlation filter ADMM

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AMTrans:Auto-Correlation Multi-Head Attention Transformer for Infrared Spectral Deconvolution

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1329-1341页

作者： Lei Gao Liyuan Cui Shuwen Chen Lizhen Deng Xiaokang Wang Xiaohong Yan Hu Zhu College of Electronic and Optical Engineering & College of Flexible Electronics(Future Technology) Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Province Key Lab on Image Processing and Image Communication Nanjing University of Posts and TelecommunicationsNanjing 210003China National Engineering Research Center of Communication and Network Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China School of Computer Science and Technology Hainan UniversityHaikou 570228China

Infrared spectroscopy analysis has found widespread applications in various fields due to advancements in technology and industry *** improve the quality and reliability of infrared spectroscopy signals,deconvolution is a crucial preprocessing *** by the transformer model,we propose an Auto-correlation Multi-head attention Transformer(AMTrans)for infrared spectrum sequence *** auto-correlation attention model improves the scaled dot-product attention in the *** utilizes attention mechanism for feature extraction and implements attention computation using the auto-correlation *** auto-correlation attention model is used to exploit the inherent sequence nature of spectral data and to effectively recovery spectra by capturing auto-correlation patterns in the *** proposed model is trained using supervised learning and demonstrates promising results in infrared spectroscopic *** comparing the experiments with other deconvolution techniques,the experimental results show that the method has excellent deconvolution performance and can effectively recover the texture details of the infrared spectrum.

关键词： spectroscopy spectral deconvolution transformer auto-correlation mechanism

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Deep Active Learning image Classification Algorithm Based on Class-Wise Self-Knowledge Distillation 6

Deep Active Learning Image Classification Algorithm Based on...

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6th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Pang, Yuliang Liu, Ying Hao, Yu Gong, Yanchao Li, Daxiang Xu, Zhijie Xi'an University of Posts and Telecommunications Center for Image and Information Processing China Xi'an University of Posts and Telecommunications Center for Image and Information Processing International Joint-Research Center for Wireless Communication and Information Processing China University of Huddersfield International Joint-Research Center for Wireless Communication and Information Processing United Kingdom

ISBN: (纸本)9798400707674

The success of image classification techniques based on deep learning relies heavily on a large amount of labeled data. However, the cost of data annotation is often expensive. This paper investigates active learning algorithms for image classification to reduce the cost of data annotation. However, traditional active learning algorithms for image classification suffer from overfitting issues due to training deep neural networks with millions of parameters using a small amount of labeled data. This makes it difficult for the model to effectively assess the information richness of unknown unlabeled samples and is detrimental to the selection of high-value samples. To alleviate these issues, this paper proposes a deep active learning-based image classification algorithm with class-wise self-knowledge distillation. This algorithm reduces overfitting and class-wise variations by matching or distilling the predictive distribution between different samples with the same label during training, thereby enabling a more accurate evaluation of the informativeness of unlabeled data by the active learning algorithm and improving the performance of the classification model. Additionally, an efficient Shuffle Attention mechanism is introduced to improve the sample selection strategy by combining spatial and channel feature information of the images. The proposed algorithm is compared with five active learning baselines on CIFAR10, CIFAR100, SVHN, and FashionMNIST datasets. Experimental results demonstrate the proposed algorithm exhibits superior classification performance. © 2023 ACM.

关键词： image classification

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ASSA-Net: Semantic Segmentation Network for Point Clouds Based on Adaptive Sampling and Self-Attention 5

ASSA-Net: Semantic Segmentation Network for Point Clouds Bas...

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5th International Conference on Natural Language processing, ICNLP 2023

作者： Ai, Da Xu, Ce Zhang, Xiaoyang Ai, Yu Bai, Yansong Liu, Ying Xi'an University of Posts and Telecommunications Center for Image and Information Processing Xi'an China Xi'an University of Posts and Telecommunications School of Communication and Information Engineering Xi'an China

ISBN: (纸本)9798350302219

Point cloud semantic segmentation is widely used in scene analysis. We propose a point cloud semantic segmentation network based on adaptive random sampling and self-attention. The network extracts local centroids using random sampling, enriches feature information of the centroids using the proposed adaptive optimization module, and then learns correlations and differences between feature vectors using a feature aggregation module based on the self-attentiveness mechanism to make feature cross-fertilization more adequate, which effectively improves the performance of semantic segmentation. Experimental results on S3DIS show that the network consumes less computing time, but improves the Mean Intersection over Union (mIou) by 14.4% and overall accuracy (oAcc) by 6.4% over the baseline network PointNet++. © 2023 IEEE.

关键词： Semantic Segmentation

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STVP: A Spatiotemporal Visual Perception Method for User-generated Content Video Quality Assessment

STVP: A Spatiotemporal Visual Perception Method for User-gen...

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2023 IEEE International Conference on Visual communications and image processing, VCIP 2023

作者： Ai, Da Lu, Mingyue Wang, Jiahao Liu, Ying Xi'An University of Posts and Telecommunications Center for Image and Information Processing Xi'an China Xi'An University of Posts and Telecommunications School of Communication and Information Engineering Xi'an China

ISBN: (纸本)9798350359855

With the popularity and development of short video applications, the behavior of using mobile devices to shoot and share user-generated content (UGC) videos has become increasingly common. Video quality assessment (VQA) is critical in guaranteeing end-user viewing experiences. UGC-VQA is a challenging problem due to the complexity and variety of distortion types of UGC videos and the absence of reference videos. To improve the consistency of UGC-VQA results and human subjective ratings, in this paper, we propose a UGC-VQA method based on spatiotemporal visual perception (STVP). Firstly, a hierarchical feature fusion module was added to the feature extraction network to realize the fusion of low-level visual features and high-level semantic features, and obtain the quality perception features with rich visual information. Then, we use the self-Attention to weight different frames to distinguish their importance. The long short-Term memory (LSTM) network and the time pool are used to model long-Term dependencies and temporal memory effects. Experimental results on UGC-VQA datasets show that the proposed method achieves a performance improvement of nearly 2%, and its evaluation results are more consistent with human visual perception. © 2023 IEEE.

关键词： Semantics

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A Fine-grained image Classification Algorithm Using Knowledge Distillation and Attention Mechanism 6

A Fine-grained Image Classification Algorithm Using Knowledg...

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6th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Feng, Hao Liu, Ying Zhang, Weidong Center for Image and Information Processing School of Communications and Information Engineering Xi'an University of Posts and Telecommunications China International Joint-Research Center for Wireless Communication and Information Processing China

ISBN: (纸本)9798400707674

Fine-grained image classification aims to classify subcategory images with large intra-class differences and small inter-class differences, and thus how to improve recognition of the detailed image information by neural network models has become a focus of scholars' research. In this paper, we used a knowledge distillation strategy based on the attention mechanism and receptive field theory to weight the feature maps of different convolutional layers and improve the recognition ability of the student models for fine-grained information. We experimentally determine the effect of embedding different locations and numbers of attention units into the student model. The experimental results show that double weighting of the channel and spatial domains of the shallow feature maps can improve the network model's ability to perceive detailed image information and improve fine-grained image classification performance. © 2023 ACM.

关键词： image classification

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Fine-grained image Classification Based on Knowledge Distillation 19

Fine-grained Image Classification Based on Knowledge Distill...

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19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2023

作者： Liu, Ying Feng, Hao Zhang, Weidong Fang, Jie Xiao, Peng Zhang, Dengsheng Xi'an University of Posts and Telecommunications Center for Image and Information Processing School of Communications and Information Engineering Shaanxi International Joint-Research Center for Wireless Communication and Information Processing Xi'an China Xi'an University of Posts and Telecommunications Center for Image and Information Processing School of Communications and Information Engineering Xi'an China Institute of Innovation Science & Sustainability Federation University Australia Gippsland Australia

ISBN: (纸本)9798350304398

Despite the outstanding performance of deep learning-based fine-grained image classification methods, the commonly used models still suffer from high cost of computation and memory Therefore, this paper proposes a mobile-based CNN network that focuses on discriminative features of fine-grained images by embedding a hybrid-domain attention module to achieve higher accuracy in recognition. Specifically, under the premise of reducing network parameters, this paper presents a classification method that combines transfer learning and knowledge distillation to enhance the model's generalization performance and resistance to overfitting. Different knowledge transfer strategies are validated through the experiments in the knowledge distillation process. Mobile models such as SqueezeNet, MobileNetV2, and CBAM MobileNetV2 all demonstrate enhanced performance the knowledge distillation optimization. The proposed method in this paper can be used to develop a lightweight mobile-based CNN model with comparable performance to complex models making it more advantageous in real-life scenarios with limited storage resources and low hardware computation levels. Additionally, the model compression process utilizes only the intermediate features of the original dataset, meeting the confidentiality requirements of the original data in the field of public security. © 2023 IEEE.

关键词： image classification

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Simultaneous Recognition Algorithm of Human Activity and Phone Position Based on Multi-sensor Data Fusion 22

Simultaneous Recognition Algorithm of Human Activity and Pho...

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4th International Symposium on Signal processing Systems, SSPS 2022

作者： Ai, Da Hao, Rui Feng, Chaolin Li, Yuxuan Liu, Ying Center for Image and Information Processing Xi'an University of Posts and Telecommunications Xi'an China School of Communication and Information Engineering Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (纸本)9781450396103

For human activity recognition based on phone sensors, the position of the phone is an important factor of the recognition accuracy. To improve the recognition accuracy of behavioral activities and the position of the phone placed, this paper proposes a classification recognition algorithm based on accelerometer and gyroscope sensors. First, sensor data collected from seven different body positions are used as inputs to a deep stacked bidirectional long and short-term memory neural network;then the activity type and the phone position are used as labels to train the neural network for simultaneous recognition of human activity and phone position;finally, the performance of the proposed method is evaluated by cross-validation. The experimental results show that placing the phone on the waist and thigh achieves the highest recognition accuracy rate. The accuracy of the simultaneous recognition of activity and position is over 90%, which is 18% higher than existing algorithms. © 2022 ACM.

关键词： Long short-term memory

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