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BEDiff: denoising diffusion probabilistic models for building extraction

Optoelectronics Letters 2025年第5期21卷 298-305页

作者： LEI Yanjing WANG Yuan CHAN Sixian HU Jie ZHOU Xiaolong ZHANG Hongkai College of Computer Science and Technology Zhejiang University of Technology Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information China Three Gorges University Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province Wenzhou University College of Electrical and Information Engineering Quzhou University School of Electronic and Information Engineering Anhui Jianzhu University

Accurately identifying building distribution from remote sensing images with complex background information is challenging. The emergence of diffusion models has prompted the innovative idea of employing the reverse denoising process to distill building distribution from these complex backgrounds. Building on this concept, we propose a novel framework, building extraction diffusion model(BEDiff), which meticulously refines the extraction of building footprints from remote sensing images in a stepwise fashion. Our approach begins with the design of booster guidance, a mechanism that extracts structural and semantic features from remote sensing images to serve as priors, thereby providing targeted guidance for the diffusion process. Additionally, we introduce a cross-feature fusion module(CFM) that bridges the semantic gap between different types of features, facilitating the integration of the attributes extracted by booster guidance into the diffusion process more effectively. Our proposed BEDiff marks the first application of diffusion models to the task of building extraction. Empirical evidence from extensive experiments on the Beijing building dataset demonstrates the superior performance of BEDiff, affirming its effectiveness and potential for enhancing the accuracy of building extraction in complex urban landscapes.

关键词： A

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DDT-Net:Deep Detail Tracking Network for Image Tampering Detection

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Computers, Materials & Continua 2025年第5期83卷 3451-3469页

作者： Jim Wong Zhaoxiang Zang Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang443002China College of Computer and Information Technology ChinaThree Gorges UniversityYichang443002China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650504China

In the field of image forensics,image tampering detection is a critical and challenging *** methods based on manually designed feature extraction typically focus on a specific type of tampering operation,which limits their effectiveness in complex scenarios involving multiple forms of *** deep learningbasedmethods offer the advantage of automatic feature learning,current approaches still require further improvements in terms of detection accuracy and computational *** address these challenges,this study applies the UNet 3+model to image tampering detection and proposes a hybrid framework,referred to as DDT-Net(Deep Detail Tracking Network),which integrates deep learning with traditional detection *** contrast to traditional additive methods,this approach innovatively applies amultiplicative fusion technique during downsampling,effectively combining the deep learning feature maps at each layer with those generated by the Bayar noise *** design enables noise residual features to guide the learning of semantic features more precisely and efficiently,thus facilitating comprehensive feature-level ***,by leveraging the complementary strengths of deep networks in capturing large-scale semantic manipulations and traditional algorithms’proficiency in detecting fine-grained local traces,the method significantly enhances the accuracy and robustness of tampered region *** with other approaches,the proposed method achieves an F1 score improvement exceeding 30% on the DEFACTO and DIS25k *** addition,it has been extensively validated on other datasets,including CASIA and *** results demonstrate that this method achieves outstanding performance across various types of image tampering detection tasks.

关键词： Image forensics image tampering detection image manipulation detection noise flow Bayar

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TSC:Efficient FRB Signal Search by a Two-stage Cascade Deep Learning Model on FAST

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Research in Astronomy and Astrophysics 2025年第3期25卷 60-69页

作者： Ronghuan Yan Junlin Li Weixin Tian Nyasha Mkwanda Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University College of Computer and Information Technology China Three Gorges University

Fast Radio Bursts(FRBs) have emerged as one of the most intriguing and enigmatic phenomena in the field of radio astronomy. The key of current related research is to obtain enough FRB signals. Computer-aided search is necessary for that task. Considering the scarcity of FRB signals and massive observation data, the main challenge is about searching speed, accuracy and recall. in this paper, we propose a new FRB search method based on Commensal Radio Astronomy FAST Survey(CRAFTS) data. The CRAFTS drift survey data provide extensive sky coverage and high sensitivity, which significantly enhance the probability of detecting transient signals like FRBs. The search process is separated into two stages on the knowledge of the FRB signal with the structural isomorphism, while a different deep learning model is adopted in each stage. To evaluate the proposed method,FRB signal data sets based on FAST observation data are developed combining simulation FRB signals and real FRB signals. Compared with the benchmark method, the proposed method F-score achieved 0.951, and the associated recall achieved 0.936. The method has been applied to search for FRB signals in raw FAST data. The code and data sets used in the paper are available at ***/aoxipo.

关键词： techniques: high angular resolution techniques: image processing techniques: interferometric

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Neural Radiance Fields for Restoring Distorted Underwater Images

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Computer engineering and Applications 2025年第9期61卷 296-303页

作者： Hua, Xifeng Huang, Zhiyong Yang, Chenlong Yao, Yu Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Hubei Yichang443000 China College of Computer and Information Technology China Three Gorges University Hubei Yichang443000 China

Neural radiance fields (NeRF) represents a burgeoning area of research that has garnered significant attention, with a primary focus on addressing challenges related to new perspective synthesis. However, the application of NeRF to underwater environments poses several challenges. In underwater scenarios, static scene images submerged under undulating water often exhibit pronounced non-rigid distortions. These distortions primarily arise from visual distortions caused by fluctuations and refraction at the water surface. To tackle the issue of underwater object image distortion, this paper conducts an in-depth analysis of the optical characteristics in underwater flow environments and the distortion of underwater object surfaces. The paper proposes a NeRF framework tailored for underwater scenes. Initially, a self-made data collection device positions the camera above the water surface, addressing the challenge of underwater data collection by simulating a multi-view virtual camera based on the periodic physical characteristics of water flow in both time and space. Subsequently, the optical flow of the images is utilized to restore the waveform shape of the water surface. Finally, employing Snell’s Law correction sampling strategy, the corrected underwater image is rendered by redefining the starting point and direction in the ray tracing process. Qualitative and quantitative evaluations of the method are conducted using a real underwater scene dataset. The results demonstrate that this approach significantly mitigates underwater image distortion, outperforming existing methods in the three-dimensional reconstruction of underwater images. The algorithm introduces novel ideas and methods for synthesizing new perspectives in underwater imagery. © 2025 Journal of Computer engineering and Applications Beijing Co., Ltd.;Science Press. All rights reserved.

关键词： Optical flows

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A leaded sine–cosine artificial protozoa optimizer algorithm for solving multi-objective optimization problems

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Cluster Computing 2025年第5期28卷 1-65页

作者： Liao, Junjie Melethil Sethumadhavan, Syam Ke, Rui Gao, Zheng-Ming Zhao, Juan College of Computer and Information Technology China Three Gorges University Yichang443002 China School of Computer Engineering Jingchu University of Technology Jingmen448000 China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang443002 China Big Data Research Center Jingchu University of Technology Jingmen448000 China School of Electronics and Information Engineering Jingchu University of Technology Jingmen448000 China

Many real-world engineering problems require simultaneous optimization of multiple objectives. The artificial protozoa optimizer (APO), a recently proposed and efficient optimization method, has demonstrated potential in solving multi-objective problems. In order to solve multi-objective problems, this paper proposes a novel algorithm based on the APO framework, the Leaded Sine Cosine Multi-objective APO (LSCMOAPO) algorithm. LSCMOAPO mainly integrates the sine–cosine algorithm (SCA) and a leader selection strategy to guide the population towards the true Pareto front. The algorithm was validated through extensive simulations involving 41 benchmark test functions (ZDT-series, UF-series, CF-series, and CEC2020-series) and five practical engineering problems. Performance was evaluated using the inverted generational distance, generational distance, Hypervolume, and spacing metrics. Comparative analysis with ten other multi-objective optimization algorithms (MOPSO, MOSSA, MOGWO, MODA, MOCryStAl, MOALO, MOGOA, MOSMA, MOGNDO, and MOAPO) also validated the better performance of LSCMOAPO in more than 86% occasions (The average of the evaluation indicators) in realizing high-quality solutions to all multi-objective problems, including linear, nonlinear, continuous, and discrete Pareto optimal front. It will set a new benchmark for new algorithm proposed. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Multiobjective optimization

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ForensiCam-215K: A Large Scale Image and Video Dataset for Forensic Analysis

ForensiCam-215K: A Large Scale Image and Video Dataset for F...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Suwen Du Pengpeng Yang Daniele Baracchi Jinglian Jin Dasara Shullani Alessandro Piva Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang China Department of Information Engineering University of Florence Florence Italy

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Determining the origin of a digital image or video, namely device source identification, is widely used in courtroom evidence and copyright protection. Currently, device source identification primarily focuses on images captured using single camera with default settings. However, with the advancement of imaging technology, there is a large number of smartphones equipped with multiple cameras and various shooting modes for acquiring images, which may pose a significant challenge to device source identification. Therefore, to assess the performance of image source identification algorithm for modern smartphones and promote further research, it is crucial to build a dataset of image and video captured by modern smartphones. In this paper, we present a large-scale image and video dataset for forensic analysis, ForensiCam-215K. The dataset includes over 215K media contents captured by 130 modern smartphones of 10 major brands. We used the latest equipment to capture images from the main, wide-angle, and telephoto cameras in six different shooting modes, and the media were collected under a strictly controlled procedure to reduce the bias caused by differences in the acquisition process between different devices. Additionally, we used the Photo Response Non-Uniformity (PRNU) method to perform device source identification tests on the dataset. The results indicate that device source identification is a challenging task especially for images and videos captured by smartphones with multiple cameras and various shooting modes. The dataset will be released as open-source and freely available for use by the multimedia forensics research community at https://***/dswdsw21072/ForensiCam-215K.

关键词： Performance evaluation Forensics Signal processing algorithms Media Streaming media Signal processing Cameras Object recognition Speech processing Smart phones

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Performance Analysis and Optimization of Probabilistic Covert Transmission Assisted by Energy Harvesting

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IEEE Internet of Things Journal 2025年

作者： Zhou, Yahui Ling, Meixian Chen, Rui Xu, Shouzhi Liu, Xiaojun China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yichang443002 China State Grid Yichang Electric Power Supply Company Yichang443002 China

In this paper, a covert wireless-powered relay (CWPR) system with the aid of a full-duplex receiver is investigated, where the receiver acts as a friendly jammer to protect the subsequent communication activities from being detected, while transmitting radio frequency (RF) energy to charge the relay. Meanwhile, both the transmitter and the relay employ a probabilistic transmission strategy to initiate information transmission based on legitimate link information. In the multi-relay scenario, two relay scheduling schemes are proposed, namely, energy harvesting maximization scheduling (EHMS) scheme and first-hop link priority scheduling (FLPS) scheme. Then, we derive the minimum detection error probability (DEP) of the eavesdropper (Eve) in two phases, and conduct a thorough analysis of the covert performance of each scheme, including covert rate, covert energy efficiency (CEE) as well as covert outage probability (COP), and obtain the optimal transmission prior probability and the optimal jamming power under each scheme. The numerical results show that the proposed probabilistic transmission strategy can significantly enhance CEE compared to the conventional continuous transmission strategy. Moreover, the proposed transmission scheme is indeed effective in enhancing the covert performance, especially when there are significant differences in channel conditions between the two-hop links. © 2014 IEEE.

关键词： Radio communication

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Many-Objective Evolutionary Algorithms for Optimization of Vehicle-Road Cooperation Systems based on intelligent Wireless Sensor Networks

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IEEE Internet of Things Journal 2025年

作者： Liu, Tingting Zhang, Lei Ding, Xuefeng China Three Gorges University School of Economics and Management Yichang443005 China China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yichang443005 China China Three Gorges University College of Computer and Information Technology Yichang443005 China

The vehicle road cooperation system (VRCS) is set to be a critical component of intelligent transportation systems. The rapid and precise acquisition of substantial multi-source traffic data is pivotal, with intelligent wireless sensor networks (IWSNs) emerging as a promising tool for developing VRCS. Nonetheless, the complexity of communication environment and the massive transmission of information have made VRCS optimization an increasingly difficult task, involving the intricate processes of coverage and routing optimization within IWSNs. This challenge essentially transforms into a very complicated many-objective optimization problem, where multiple conflicting objectives and resource constraints need to be tackled simultaneously. Traditional optimization methods struggle to offer satisfactory solutions. Our study introduces a many-objective evolutionary algorithm that concurrently manages coverage and routing optimization in IWSNs. Initially, we present a three-dimensional sensing model to delineate the perception range of an individual sensor. Subsequently, we propose a comprehensive four-objective optimization model, encompassing coverage, connectivity, energy consumption, and deployment cost, to accurately depict the performance of IWSNs. Especially this integrated model includes a swift connectivity evaluation method and a specialized routing forwarding strategy. Furthermore, we suggest a node sleeping strategy to minimize energy consumption further. To find a solution to the resultant many-objective optimization problem, which refers to numerous objectives and constraints, we developed a swarm optimization algorithm based on rapid fitness evaluation strategy and differential co-evolution, aimed at boosting convergence speed and accuracy of the proposed approach. Comparative experiments indicate that our model and algorithm outperform other algorithms in terms of efficiency and effectiveness. © 2025 IEEE.

关键词： Optimization algorithms

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MiT-Unet: Mixed Transformer Unet for Transmission Line Segmentation in UAV Images

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IEEE Transactions on Power Delivery 2025年

作者： Wei, Ning Chen, Jianwei Sun, Shuifa The Hubei Key Laboratory of Intelligent Vision-Based Monitoring for Hydro electric Engineering College of Computer and Information Technology China Three Gorges University Yichang443002 China The Engineering Research Center of Mobile Health Management System Ministry of Education Hangzhou Normal University China

The segmentation of power lines in drone images is one of the challenging tasks in the field of computer vision. Although power lines share the same difficulties with tiny object segmentation, occupying only a very small proportion of pixel areas in the images, the greater challenge is that they also have a very large visual perspective field. Therefore, the results obtained by traditional convolutional neural network-based segmentation methods are still unsatisfactory. To tackle these problems, we propose MiT-Unet (Mixed Transformer Unet), which has an analogous multi-level convolutional neural network structure similar to Unet for encoding and decoding transmission line detailed features. Meanwhile, dealing with excessive scales, we employ an Efficient Self-Attention-based module to enhance the global features of straight lines. Furthermore, in the level transition of this architecture, we propose simple yet efficient upsampling and downsampling modules, TLFM and TLFE, to fuse the global attention feature of power lines. These modules effectively extract and preserve the vulnerable features of power lines during the level transition process. Experimental results demonstrate that our proposed method achieves state-of-the-art performance in transmission line segmentation on the public TTPLA dataset and PLDU dataset. Moreover, the computational efficiency of the proposed model makes it potentially deployable on mobile platforms. The testing code has been released at: https://***/xiachengs/MiT-Unet. ©1986-2012 IEEE.

关键词： Image segmentation

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An intelligent First Arrival Picking Method of Microseismic Signals based on the Small Sample Expansion

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IEEE Transactions on Geoscience and Remote Sensing 2025年

作者： Huang, Wenxuan Sheng, Guanqun Tang, Xingong Ma, Kai Lu, Jingyi Sun, Hang China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yichang443002 China East China Normal University School of Computer Science and Technology Shanghai China Yangtze University School of Geophysics and Petroleum Resources Wuhan443000 China China University of Geosciences School of Water Resources and Environment Beijing100083 China

An accurate and efficient first arrival picking method of microseismic signals is essential for improving the microseismic recording processing performance. However, weak seismic magnitude and low signal-to-noise ratio (SNR) of microseismic signals make highly accurate and efficient microseismic signal processing challenging when traditional signal picking methods are used. Therefore, to use microseismic signal features effectively and improve both the accuracy and the efficiency of the first arrival picking of microseismic signals, this paper proposes a first arrival picking method of microseismic signals based on deep learning. In addition, a generative adversarial network with latent space and adaptive layer-instance normalization for microseismic signals generation (G-LA-MSG) is used to generate a large number of effective microseismic signals under unsupervised conditions to expand the microseismic data having a limited number of samples. On the basis of the sample expansion, an AND-OR grammar pyramid scene parsing network (AOG-PSPNet) is designed to select the first arrivals of microseismic signals. By adopting the AND-OR grammar block (AOGBlock) structure, the network can fully extract the sequence data features, achieve good SNR separation in a low SNR environment, and improve the accuracy of first arrivals of low SNR microseismic signals. The results of the arrival picking experiments conducted under the condition of a low SNR for synthetic microseismic records and real microseismic records show that the proposed method can achieve a superior performance compared to the commonly used deep learning-based methods for first arrival picking of microseismic signals. © 2025 IEEE.

关键词： Microseismic monitoring

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