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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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GLNet: Low-light image enhancement via grayscale priors 13

GLNet: Low-light image enhancement via grayscale priors

引用

13th International conference on Signal processing systems, ICSPS 2021

作者： Guo, Li xie, Junwei Xue, Yuyang Li, Ru Zheng, Weixin Tong, Tong Gao, Qinquan College of Physics and Information Engineering Fuzhou University Tsukuba Japan Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology Fuzhou University Tsukuba Japan Imperial Vision Technology Fujian China Department of Computer Science University of Tsukuba Tsukuba Japan

ISBN: (数字)9781510653184

ISBN: (纸本)9781510653177

Low-light images are generally produced by shooting in a low light environment or a tricky shooting angle, which not only affect people's perception, but also leads to the bad performance of some artificial intelligence algorithms, such as object detection, super-resolution, and so on. There are two difficulties in the low-light enhancement algorithm: in the first place, applying image processing algorithms independently to each low-light image often leads to the color distortion;the second is the need to restore the texture of the extremely low-light area. To address these issues, we present two novel and general approaches: firstly, we propose a new loss function to constrain the ratio between the corresponding RGB pixel values on the low-light image and the high-light image;secondly, we propose a new framework named GLNet, which uses the dense residual connection block to obtain the deep features of the low-light images, and design a gray scale channel network branch to guide the texture restoration on the RGB channels by enhancing the grayscale image. The ablation experiments have demonstrated the effectiveness of the proposed module in this paper. Extensive quantitative and perceptual experiments show that our approach obtains state-of-the-art performance on the public dataset. © COPYRIGHT SPIE.

关键词： image enhancement

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Multi-label Text Classification Based on BiGRU and Multi-Head Self-Attention Mechanism

Multi-label Text Classification Based on BiGRU and Multi-Hea...

引用

image processing and Media Computing (ICIPMC), International conference on

作者： Tongtong Luo Nan Shi Meilin Jin Aolong Qin Jiacheng Tang xihan Wang Quanli Gao Lianhe Shao School of Computer Science Xi’an Polytechnic University Xi’an China

ISBN: (数字)9798350386660

ISBN: (纸本)9798350386677

Multi-label text classification (MLTC) is widely used in natural language processing. There are correlations among labels in most MLTC tasks. But traditional multi-label text classification algorithms tend to pay more attention to the information of the text itself, and the correlations among labels are not sufficiently extracted, which largely affects the classification performance of the model. Aiming at the above problems, this paper proposes a multi-label text classification method based on BiGRU and multi-head self-attention mechanism. The method preprocesses text information by word embedding tools, and extracts contextual and global feature information of text using BERT and BiGRU models. The dependencies of different subspace labels are also integrated with a multi-head self-attention mechanism to model the correlation among labels. Label prediction is performed by combining label feature information and text feature information to realize the multi-label classification task. Experimental results on AAPD and Reuters-21578 datasets show that the method can effectively improve the results of text classification.

关键词： Correlation Text categorization Media Feature extraction Natural language processing Classification algorithms Data mining

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S2P: State-conditioned image Synthesis for Data Augmentation in Offline Reinforcement Learning 36

S2P: State-conditioned Image Synthesis for Data Augmentation...

引用

36th conference on Neural Information processing systems (NeurIPS)

作者： Cho, Daesol Shim, Dongseok Kim, H. Jin Seoul Natl Univ Automat & Syst Res Inst ASRI Seoul South Korea Seoul Natl Univ Interdisciplinary Program AI Seoul South Korea Seoul Natl Univ Artificial Intelligence Inst Seoul Natl Univ AIIS Seoul South Korea

ISBN: (纸本)9781713871088

Offline reinforcement learning (Offline RL) suffers from the innate distributional shift as it cannot interact with the physical environment during training. To alleviate such limitation, state-based offline RL leverages a learned dynamics model from the logged experience and augments the predicted state transition to extend the data distribution. For exploiting such benefit also on the image-based RL, we firstly propose a generative model, S2P (State2Pixel), which synthesizes the raw pixel of the agent from its corresponding state. It enables bridging the gap between the state and the image domain in RL algorithms, and virtually exploring unseen image distribution via model-based transition in the state space. Through experiments, we confirm that our S2P-based image synthesis not only improves the image-based offline RL performance but also shows powerful generalization capability on unseen tasks.

关键词： Reinforcement learning

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A Robotic Approach for Crack Detection through the Integration of Cameras and LiDARs

A Robotic Approach for Crack Detection through the Integrati...

引用

conference on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace systems

作者： Alamdari, Ali Ghadimzadeh Ebrahimkhanlou, Arvin Drexel Univ Dept Mech Engn & Mech 3141 Chestnut St Philadelphia PA 19104 USA

ISBN: (纸本)9781510660793;9781510660809

Structural health monitoring is crucial for ensuring the safety of civil infrastructure, and crack detection is an essential component of this process. Cameras provide high-resolution images of the structure's surface, which can be analyzed to detect and locate cracks. LiDAR sensors use laser beams to scan the surface of the structure and produce detailed 3D point clouds that can be used to detect cracks and measure their dimensions. The proposed approach aims to improve the accuracy and efficiency of crack detection in SHM by integrating the complementary strengths of cameras and LiDARs in a simulation environment. The approach involves the use of an intelligent algorithm that can automatically fuse the data from the cameras and LiDARs to produce a more comprehensive and accurate representation of surface cracks. The algorithm uses a machine learning-based crack detection technique that can accurately identify and locate cracks in real-time. Furthermore, a depth camera is used to provide a denser point cloud than LiDAR of the crack. The integration of cameras and LiDARs for crack detection in SHM offers several advantages, such as improved accuracy, faster data acquisition, and reduced costs compared to traditional methods. The proposed approach addresses the challenges of data fusion, image processing, and intelligent algorithm development by offering a novel solution that leverages the strengths of both cameras and LiDARs. The findings of this study suggest that the proposed approach can significantly enhance the capabilities of SHM for crack detection. The approach offers a more accurate and efficient way of detecting cracks in real-time, which can help prevent further damage and ensure the safety of civil infrastructure.

关键词： Structural health monitoring cameras LiDAR crack detection data fusion image processing algorithms intelligent systems

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A Nonconvex Total Variation Based Method for SAR image Despeckling 18th

A Nonconvex Total Variation Based Method for SAR Image Despe...

引用

18th International conference on Intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2022

作者： Wang, Chao Guo, Baolong Li, Cheng Institute of Intelligent Control and Image Engineering Xidian University Xi’an China

ISBN: (纸本)9789819906048

Speckle noise usually deteriorate the synthetic aperture radar (SAR) imaging system severely, which results in great difficulties to the post-processing. In this paper, we propose a novel despeckling method based on Fisher-Tippett (FT) distribution and nonconvex total variation (NTV) model to tackle the problem. First, we introduce a FT distribution-based Bayesian framework to develop speckle noise removal models. Secondly, we propose a new nonconvex TV model which is more appropriate to preserve edges/textures than the conventional model. Meanwhile, we give a simple method to solve the nonconvex TV model. Thirdly, a new iterative regularization scheme-based despeckling algorithm is proposed, in which the NTV model filtered residual is added back to the despeckling image. Experiments conducted on both simulated and real SAR images demonstrate that the proposed method can effectively remove speckle while preserving edges and textures compared with some state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Speckle

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Attention-Based Grayscale image Colorization 20

Attention-Based Grayscale Image Colorization

引用

20th IEEE International conference on Mechatronics and Automation, ICMA 2023

作者： Sun, Lei Ju, Dongjie Ke, Shi School of Electrical Engineering and Automation Binshui Xidao Extension 391 Tianjin300384 China Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Complicated Industry Systems Binshui Xidao Extension 391 Tianjin300384 China

ISBN: (纸本)9798350320831

image colorization has always been a hot topic in computer vision. Since the emergence of deep learning and its excellent performance in many image-processing tasks, image colorization methods based on convolutional neural networks (CNN) emerge in an endless stream, including automatic colorization methods and A coloring method based on a reference image. In the coloring method based on the reference image, an existing problem is that an algorithm for calculating the similarity between the grayscale image and the reference image needs to be designed to map the color blocks in the reference image to the grayscale image. And we use the attention mechanism to spread color implicitly, and propose an attention-guided image colorization network, which extracts features and corresponding color information from the reference image through a reference encoding sub-network, and simultaneously passes a target encoding sub-network The network extracts grayscale features from grayscale images, uses attention to calculate the correlation between the two types of features, and then restores the features and reconstructs colors through the decoding network. Our method can quickly capture the color of the reference feature, accurately transfer it to the target feature, and effectively solve the problem of small target loss during downsampling. By validating on multiple datasets, the results show that our method is effective, outperforming multiple state-of-the-art image colorization algorithms. © 2023 IEEE.

关键词： Color

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Physics-Constrained Comprehensive Optical Neural Networks 38

Physics-Constrained Comprehensive Optical Neural Networks

引用

38th conference on Neural Information processing systems, NeurIPS 2024

作者： Liu, Yanbing Qin, Jianwei Liu, Yan Yue, xi Liu, Xun Wang, Guoqing Li, Tianyu Ye, Fangwei Li, Wei Beijing University of Posts and Telecommunications Beijing100876 China Shanghai Jiao Tong University Shanghai200240 China Beijing Institute of Space Mechanics and Electricity Beijing100094 China University of Electronic Science and Technology of China Chengdu611731 China

With the advantages of low latency, low power consumption, and high parallelism, optical neural networks (ONN) offer a promising solution for time-sensitive and resource-limited artificial intelligence applications. However, the performance of the ONN model is often diminished by the gap between the ideal simulated system and the actual physical system. To bridge the gap, this work conducts extensive experiments to investigate systematic errors in the optical physical system within the context of image classification tasks. Through our investigation, two quantifiable errors-light source instability and exposure time mismatches-significantly impact the prediction performance of ONN. To address these systematic errors, a physics-constrained ONN learning framework is constructed, including a well-designed loss function to mitigate the effect of light fluctuations, a CCD adjustment strategy to alleviate the effects of exposure time mismatches and a'physics-prior-based' error compensation network to manage other systematic errors, ensuring consistent light intensity across experimental results and simulations. In our experiments, the proposed method achieved a test classification accuracy of 96.5% on the MNIST dataset, a substantial improvement over the 61.6% achieved with the original ONN. For the more challenging QuickDraw16 and Fashion MNIST datasets, experimental accuracy improved from 63.0% to 85.7% and from 56.2% to 77.5%, respectively. Moreover, the comparison results further demonstrate the effectiveness of the proposed physics-constrained ONN learning framework over state-of-the-art ONN approaches. This lays the groundwork for more robust and precise optical computing applications. © 2024 Neural information processing systems foundation. All rights reserved.

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Reconstruction quality evaluation of Compressed Sensing image Mapping Spectrometer

Reconstruction quality evaluation of Compressed Sensing Imag...

引用

conference on AOPC - Computing Imaging Technology

作者： Yang, Shuya Ding, xiaoming Yuan, Hao Lu, Dunqiang Yan, Qiangqiang Tianjin Normal Univ Tianjin Key Lab Wireless Mobile Commun & Power Tr Tianjin 300387 Peoples R China Shaanxi Key Lab Opt Remote Sensing & Intelligent Xian 710119 Peoples R China Xian Inst Opt & Precis Mech CAS Key Lab Spectral Imaging Technol Xian 710119 Peoples R China

ISBN: (纸本)9781510672413;9781510672406

This paper uses traditional algorithms and deep learning algorithms to recover datacube obtained by CASSI and CSIMS in order to verify that CSIMS outperforms CASSI by comparing the Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM) and Relative spectral Quadratic Error (RQE) of the reconstructed datacube. The experimental results show that the datacube of CASSI and CSIMS can be both reconstructed by ADMM- TV algorithm which is the most effective among the traditional algorithms. PSNR of the reconstructed datacube of CASSI is 32.50 dB, while that of CSIMS is 35.53 dB, with an increase of 3.03 dB. By using deep learning algorithm, both systems improve substantially under the PnP-HSI network, with PSNR of CASSI growing to 38.85 dB and that of CSIMS growing to 41.97 dB, which can be seen that CSIMS is still 3.12 dB higher than CASSI.

关键词： Snapshot spectral imaging Compression sensing image mapper Deep learning

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Pre-Trained Model Reusability Evaluation for Small-Data Transfer Learning 36

Pre-Trained Model Reusability Evaluation for Small-Data Tran...

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36th conference on Neural Information processing systems (NeurIPS)

作者： Ding, Yao-xiang Wu, xi-Zhu Zhou, Kun Zhou, Zhi-Hua Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9781713871088

We study model reusability evaluation (MRE) for source pre-trained models: evaluating their transfer learning performance to new target tasks. In special, we focus on the setting under which the target training datasets are small, making it difficult to produce reliable MRE scores using them. Under this situation, we propose synergistic learning for building the task-model metric, which can be realized by collecting a set of pre-trained models and asking a group of data providers to participate. We provide theoretical guarantees to show that the learned task-model metric distances can serve as trustworthy MRE scores, and propose synergistic learning algorithms and models for general learning tasks. Experiments show that the MRE models learned by synergistic learning can generate significantly more reliable MRE scores than existing approaches for small-data transfer learning.

关键词： Data transfer

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Design of Human Resource Management System Based on Ant Colony Algorithm

Design of Human Resource Management System Based on Ant Colo...

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Information Technology, Electronics and Intelligent Communication systems (ICITEICS), IEEE International conference on

作者： Yanling Jia Yao xing Xi'an Polytechnic University School of Management Shaanxi China

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

Aiming at the limitations of traditional image recognition algorithms in the design of human resource management system, a design scheme of resource management system based on ant colony algorithm was proposed. Firstly, the influencing factors is accurately located through the colony foraging theory, and the indicators is reasonably divided to reduce interference, and the ant colony algorithm is used to construct the design scheme of the resource management system. Experimental results show that under certain evaluation criteria, the proposed scheme is superior to the traditional image recognition algorithm in terms of the design accuracy of the resource management system and the processing time of influencing factors, and has obvious advantages. The design of resource management system plays an extremely important role in human resources, which can accurately predict and optimize the growth characteristics and product generation of human resources. However, traditional image recognition algorithms have certain limitations in solving resource management simulation problems, especially when dealing with complex problems. In this paper, a resource management system design scheme based on ant colony algorithm is proposed to better solve this problem. In this scheme, the influencing factors were accurately located through the swarm foraging theory, so as to determine the division of indicators, and the ant colony algorithm was used to construct the scheme. Experimental results show that under certain evaluation criteria, the accuracy and speed of the scheme is significantly improved for different problems, and it has better performance. Therefore, the simulation scheme based on ant colony algorithm in the design of human resource management system can better solve the limitations of traditional image recognition algorithms and improve the simulation accuracy and efficiency.

关键词： Accuracy image recognition Key performance indicator Interference Prediction algorithms Resource management Human resource management

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