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Enhancing defocus blur in three-dimensional scenes via high-speed optical axial scanning and deep learning 2

Enhancing defocus blur in three-dimensional scenes via high-...

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2nd International Academic conference on Optics and Photonics, IACOP 2024

作者： Li, Zilong Tang, Chenxi Dong, Jiaqing Liu, Xuan Lin, Jiabin Liu, Qiegen Song, Xianlin School of Information Engineering Nanchang University Nanchang330031 China Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications Nanchang University Nanchang330031 China

ISBN: (数字)9781510688827

ISBN: (纸本)9781510688810

Mitigating image defocus blur induced by the limited depth of field in imaging systems and reconstructing the three-dimensional depth of scenes represents a significant hurdle. In this paper, a method for enhancing defocus blur in three-dimensional scenes via high-speed optical axial scanning and deep learning was proposed. The optical axial scanning system functions by synchronizing a liquid electronically tunable lens and a CCD camera to capture multi-focus image sequences. The system efficiently captures real-world images with a fast acquisition time of ~50 ms per image. The multi-focus image sequences are then fused to all-in-focus images. The multi-focus images and all-in-focus images (ground truth) serve as the dataset for the deep learning neural network. Defocused images can be reconstructed using the trained deep learning network. Furthermore, by establishing a direct relationship between the focal depth and the driving current applied to the liquid electronically tunable lens, the three-dimensional depth of the scene can be rapidly determined. Experimental results validate the feasibility and effectiveness of this method. It is expected to expand the applications of image defocus enhancement in fields such as medical imaging, 3D shape measurement, and augmented reality. © 2025 SPIE.

关键词： CCD cameras

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Enhanced Discrete Cosine Transform image Compression for Ultra-High-Resolution imagery

Enhanced Discrete Cosine Transform Image Compression for Ult...

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International conference on Power Engineering and Intelligent Systems, PEIS 2024

作者： Narlagiri, Shilpa Malathy, V. Vanamala, Kedhareshwar Rao Ganja, Sri Sai Sathyanarayana SR University Warangal India

ISBN: (纸本)9789819767090

This work presents a specialized image compression method as enhanced discrete cosine transform (DCT) that is designed specifically for ultra-high-resolution imagery. By using the mathematical principles of DCT, our method enhances the compression procedure to effectively handle the difficulties linked to high-resolution photographs, all while preserving essential picture characteristics. This approach entails dynamically selecting block sizes and independently processing color channels, so ensuring the preservation of color information. An important advancement is the use of coefficient thresholding, which involves selecting setting to zero the DCT coefficients that exceed a predetermined threshold. This technique allows for substantial decrease in data while maintaining visual quality. The suggested methodology follows a methodical procedure, enabling users to choose a compression factor that optimizes the trade-off between image quality and compression ratio. The process encompasses every stage, starting with the analysis of the initial RGB (Red, Green, Blue) image to the restoration of the compressed RGB picture. This includes the calculation of DCT coefficients, the choice of block sizes, and the implementation of thresholding for compression. The procedure culminates with the possibility of assessment and optimization, allowing users to refine settings according to defined standards for image quality. The approach is a reasonable and adaptable solution for many applications involving the compression of high-resolution images. This approach allows for the reduction of data while maintaining important image features. The experimental findings showcase the versatility of the method, rendering it appropriate for a wide range of ultra-high-resolution imaging settings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image compression

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DIM: Dynamic Integration of Multimodal Entity Linking with Large Language Model 7th

DIM: Dynamic Integration of Multimodal Entity Linking with L...

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7th Chinese conference on Pattern Recognition and Computer Vision

作者： Song, Shezheng Li, Shasha Yu, Jie Zhao, Shan Li, Xiaopeng Ma, Jun Liu, Xiaodong Li, Zhuo Mao, Xiaoguang Natl Univ Def Technol Kaifu Peoples R China Hefei Univ Technol Hefei Peoples R China

ISBN: (纸本)9789819786190;9789819786206

Our study delves into Multimodal Entity Linking, aligning the mention in multimodal information with entities in knowledge base. Existing methods are still facing challenges like ambiguous entity representations and limited image information utilization. Thus, we propose dynamic entity extraction using ChatGPT, which dynamically extracts entities and enhances datasets. We also propose a method: Dynamically Integrate Multimodal information with knowledge base (DIM), employing the capability of the Large Language Model (LLM) for visual understanding. The LLM, such as BLIP-2, extracts information relevant to entities in the image, which can facilitate improved extraction of entity features and linking them with the dynamic entity representations provided by ChatGPT. The experiments demonstrate that our proposed DIM method outperforms the majority of existing methods on the three original datasets, and achieves state-of-the-art (SOTA) on the dynamically enhanced datasets (Wiki+, Rich+, Diverse+). For reproducibility, our code and collected datasets are released on https://***/season1blue/DIM.

关键词： Multimodal information processing Large language model Object recognition Multimodal entity linking

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Underwater Salient Object Detection Based on Swintransformer 5th

Underwater Salient Object Detection Based on Swintransformer

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5th China Intelligent Robotics Academic conference

作者： Jiao, Zhan Wang, Ruizi Fu, Bo Zhang, Tianzhuang Zhang, Ning Liaoning Vocat Coll Light Ind Dalian 116100 Peoples R China Liaoning Normal Univ Sch Comp Sci & Artificial Intelligence Dalian 116081 Peoples R China Northeast Normal Univ Training Ctr Minist Educ Studying Overseas Changchun Peoples R China

ISBN: (纸本)9789819616138;9789819616145

Underwater Salient Object Detection (USOD) has emerged as a specialized division of Salient Object Detection (SOD) to meet the increasing demand for underwater exploration. However, two major challenges hinder its development. First, current USOD datasets are limited in size and diversity, making them insufficient for robust model training and evaluation. Second, most existing deep learning-based USOD approaches rely on convolutional encoder-decoder architectures, which improve inference over traditional methods but still suffer from limited receptive fields and high computational costs. To address these challenges, we introduce a new USOD method based on the Swin transformer, which incorporates a multimodal feature fusion module to increase detection accuracy. Our model uses RGB images and depth maps to capture both semantic information and visual details. The multimodal feature fusion module dynamically adjusts feature importance through attention mechanisms, enhancing the ability of the model to extract meaningful information. By using a Swin transformer model with pretrained weights as the encoder backbone, the proposed method mitigates data scarcity issues and captures both the global context and long-range dependencies. The experimental results demonstrate that our approach achieves significant improvements in detecting salient objects in underwater environments.

关键词： Underwater image Salient object detection Swin transformer

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ATTIQA: Generalizable image Quality Feature Extractor Using Attribute-Aware Pretraining 17th

ATTIQA: Generalizable Image Quality Feature Extractor Using ...

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17th Asian conference on Computer Vision, ACCV 2024

作者： Kwon, Daekyu Kim, Dongyoung Ki, Sehwan Jo, Younghyun Lee, Hyong-Euk Kim, Seon Joo Yonsei University Seoul Korea Republic of Samsung Advanced Institue of Technology Suwon-si Korea Republic of

ISBN: (纸本)9789819609109

In no-reference image quality assessment (NR-IQA), the challenge of limited dataset sizes hampers the development of robust and generalizable models. Conventional methods address this issue by utilizing large datasets to extract rich representations for IQA. Also, some approaches propose vision language models (VLM) based IQA, but the domain gap between generic VLM and IQA constrains their scalability. In this work, we propose a novel pretraining framework that constructs a generalizable representation for IQA by selectively extracting quality-related knowledge from VLM and leveraging the scalability of large datasets. Specifically, we select optimal text prompts for five representative image quality attributes and use VLM to generate pseudo-labels. Numerous attribute-aware pseudo-labels can be generated with large image datasets, allowing our IQA model to learn rich representations about image quality. Our approach achieves state-of-the-art performance on multiple IQA datasets and exhibits remarkable generalization capabilities. Leveraging these strengths, we propose several applications, such as evaluating image generation models and training image enhancement models, demonstrating our model’s real-world applicability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： visual languages

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Cyclic Learning of a Frame Downsampler and a Recognion Model in High-Speed Camera image Recognition 27th

Cyclic Learning of a Frame Downsampler and a Recognion M...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Namiki, Shigeaki Ogawa, Takuya Yokoyama, Keiko Yachida, Shoji Hosoi, Toshinori NEC Corporation Kanagawa Japan

ISBN: (纸本)9783031784460

High-speed cameras capture instantaneous changes in dynamic phenomena, enabling new image classification applications such as nonstop visual inspection of dynamically moving objects. However, due to high-speed cameras’ high frame rate, downsampling excess frames or limiting the size of the recognition model is necessary for real-time processing. Previous work has introduced a frame downsampler (a binary classifier optimized to predict in advance whether the output of a recognition model is true or false) and applied the downsampler to retain high-scoring frames for the recognition model. However, further optimization of the recognition model for the sampled data distribution is unexplored and remains sub-optimal. In this study, we propose "cyclic learning" for high-speed camera image recognition. It optimizes the recognition model for the data distribution left by the downsampler and retrains the downsampler based on the updated recognition model. We constructed a dataset of fast-moving objects captured by a high-speed camera to classify object types, and experimental results on this dataset proved that the proposed method outperformed previous studies regarding overall classification performance with the same number of samples and the number of samples required for comparable classification performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： High speed cameras

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Spectral Aggregation Cross-Square Transformer for Hyperspectral image Denoising 27th

Spectral Aggregation Cross-Square Transformer for Hyperspect...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Liu, Yang Ji, Yantao Xiao, Jiahua Guo, Yu Jiang, Peilin Yang, Haiwei Wang, Fei School of Software Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

ISBN: (纸本)9783031783531

Hyperspectral image(HSI) denoising addresses noise impact during image acquisition. Transformers have gained notable prominence in the field of denoising, but their quadratic self-attention complexity poses computational challenges, hindering global information processing. Classical window-based self-attention limits non-local information flow, hampering large-scale object capture in HSI. Furthermore, spectral variations among neighboring bands introduce redundancy, which burdens the model and diminishes token variability, resulting in over-smoothing in the attention map. To address these issues, we propose a novel method, named Spectral Aggregation Cross-Square Transformer(SACT). We introduce a cross-square self-attention mechanism to enhance information exchange between windows, capturing long-range dependencies within spatial intra-spectrum from multiple perspectives. Spectrally, it extends the attention region horizontally, surrounding, and vertically, exploring omnidirectional spatial correlations among different receptive windows. Additionally, a spatial-spectral aggregation self-attention module is designed to capture global contextual dependencies across spatial and spectral dimensions, reducing spectral redundancy computation. Our method has evaluated synthetic and real hyperspectral datasets and shows SACT’s effectiveness in enhancing both quantitative and qualitative HSI denoising performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image denoising

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Deep Learning Approach for Automated Railroad Ballast Condition Evaluation 5th

Deep Learning Approach for Automated Railroad Ballast Condit...

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5th International conference on Transportation Geotechnics

作者： Luo, Jiayi Ding, Kelin Qamhia, Issam I. A. Hart, John M. Tutumluer, Erol Univ Illinois Dept Civil & Environm Engn 205 N Mathews Urbana IL 61801 USA Univ Illinois Beckman Inst Adv Sci & Technol 405 N Mathews Urbana IL 61801 USA

ISBN: (纸本)9789819782154;9789819782130;9789819782123

Ballast has a significant impact on track performance, and the evaluation of ballast condition is crucial for safe railroad operations. This paper focuses on a Ballast Scanning Vehicle (BSV) recently developed for automating ballast inspection using a deep learning-based, computer vision approach. Traditional evaluation methods, e.g., visual inspection or ballast sampling followed by sieve analysis, are subjective and labor-intensive. Furthermore, ballast samples collected from a single location/depth may not be representative of accurately revealing variations of degradation and ballast condition along the track. In contrast, the BSV employs three image acquisition devices to continuously capture high-quality scans of ballast cut sections, enabling accurate and in-depth evaluation of continuous sections of the track. The deep-learning framework was trained to process acquired ballast scans, generating image-based metrics including percent degraded segments (PDS), fouling index (FI) estimates, and in-service ballast gradations. The accompanying user-friendly graphical interface integrates all data processing algorithms and provides comprehensive visualizations of results. Field data was collected using the BSV, from cut trenches opened using a ballast regulator, along the High Tonnage Loop (HTL) at the Transportation Technology Center (TTC) in the U.S. The FI and gradations from the BSV were compared to laboratory sieve analyses and Ground Penetrating Radar (GPR) data. Additional laboratory tests with various fouling conditions were conducted to validate the deep learning algorithms and clarify any differences between sieving results and algorithm estimates possibly attributed to sampling issues. This field deployment demonstrated that the BSV could accurately evaluate ballast conditions close to real time, thus making it a robust system for quantifying ballast degradation.

关键词： Railroad Ballast Fouling Inspection Track vehicle image segmentation Deep learning

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Research on Dam Crack Identification Method Based on Multi-source Information Fusion

Research on Dam Crack Identification Method Based on Multi-s...

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International Hydropower Development conference, IHDC 2024

作者： Xin, Cun Yang, Dangfeng Liu, Xiaodong Huang, Yong Qian, Xueming Power China Northwest Engineering Corporation Limited Xi’an Key Laboratory of Clean Energy Digital Technology Xi’an710065 China State Engineering Laboratory of Visual Information Processing and Application Xi’an Jiaotong University Xi’an710049 China

ISBN: (纸本)9789819791835

Cracks as the main safety concern of dams, high-precision identification of dam cracks is of great application value and scientific significance to ensure the safety of dams. The paper proposes a dam crack identification method based on multi-source information fusion. Specifically, image gray scale and geometric features are extracted based on the image information. And then a single crack identification model based on Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), XGBoost, and BP Neural Network are established based on the features, respectively. Finally, a multi-classifier fusion algorithm based on D-S evidence theory is established to identify the presence of cracks by fusing single identification models. Experiments are carried out to compare the proposed method with the existing identification methods based on the evaluation metrics such as accuracy, precision, F1-score, and recall. The results show that the accuracy of crack identification of the proposed method in this paper reaches 98.9%, and the crack identification results are better than the existing methods. © The Author(s) 2025.

关键词： Concrete dams

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3rd International conference on Computing and Communication Networks, ICCCN 2023

3rd International Conference on Computing and Communication ...

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3rd International conference on Computing and Communication Networks, ICCCN 2023

ISBN: (纸本)9789819726707

The proceedings contain 60 papers. The special focus in this conference is on Computing and Communication Networks. The topics include: Enhancing Security in Wireless Sensor Networks: A Broadcast/Multicast Authentication Framework with Identity-Based Signature Schemes;TSFTO: A Two-Stage Fuzzy-Based Tasks Orchestration Algorithm for Edge and Fog Computing Environments;tracking Climatic Variations Through Smart IoT-Driven Approach: An Exploratory Analysis;Design and Manufacturing of an Efficient Low-Cost Fire Surveillance System Based on GSM communications and IoT Technology;a Comparative Analysis of Optimized Routing Protocols for High-Performance Mobile Ad Hoc Networks;integrating Sandpiper Optimization Algorithm and Secure Aware Routing Protocol for Efficient Cluster Head Selection in Wireless Sensor Networks;the Effects of Weather Conditions on Data Transmission in Free Space for Wireless Sensor Networks;Using Wireless Sensor Network Performance and Optimizing in Underground Mines with Virtual MIMO Antenna;a Novel Logistic Regression-Based Fire Detection Model Using IoT in Underground Coal Mines;An MQTT IoT Intrusion Detection System Using Deep-Learning;multi-objective Optimal Feature Selection for Cyber Security Integrated with Deep Learning;application of Gradient Boosting Classifier-Based Computational Intelligence to Detect Drug Addiction Threat in Society;insurance 4.0: Smart Insurance Technologies and Challenges Ahead;review on Vision Transformer for Satellite image Classification;primitive Roots and Euler Totient Function-Based Progressive visual Cryptography;approach for Fire Detection Using image processing;review of Various Approaches for Authorship Identification in Digital Forensics;machine Learning Model for Traffic Prediction and Pattern Extraction in High-Speed Optical Networks;improved Markov Decision Process in Wireless Sensor Network for Optimal Energy Consumption.

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