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CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement

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arXiv 2024年

作者： Wu, Xu Hou, XianXu Lai, Zhihui Zhou, Jie Zhang, Ya-Nan Pedrycz, Witold Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China The Department of Electrical & Computer Engineering University of Alberta University of Alberta Canada

Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations;(2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-Resolution

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arXiv 2024年

作者： Xu, Tianyi Zhou, Yijie Hu, Xiaotao Zhang, Kai Zhang, Anran Qiu, Xingye Xu, Jun School of Statistics and Data Science Nankai University Tianjin China College of Computer Science Nankai University Tianjin China School of Intelligence Science and Technology Nanjing University Suzhou China Tencent Data Platform Beijing China Zhejiang University Hangzhou China Systems Engineering Research Institute China State Shipbuilding Corporation Limited Beijing China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen China

Arbitrary-scale super-resolution (ASSR) aims to learn a single model for image super-resolution at arbitrary magnifying scales. Existing ASSR networks typically comprise an off-the-shelf scale-agnostic feature extractor and an arbitrary scale upsampler. These feature extractors often use fixed network architectures to address different ASSR inference tasks, each of which is characterized by an input image and an upsampling scale. However, this overlooks the difficulty variance of super-resolution on different inference scenarios, where simple images or small SR scales could be resolved with less computational effort than difficult images or large SR scales. To tackle this difficulty variability, in this paper, we propose a Task-Aware Dynamic Transformer (TADT) as an input-adaptive feature extractor for efficient image ASSR. Our TADT consists of a multi-scale feature extraction backbone built upon groups of Multi-Scale Transformer Blocks (MSTBs) and a Task-Aware Routing Controller (TARC). The TARC predicts the inference paths within feature extraction backbone, specifically selecting MSTBs based on the input images and SR scales. The prediction of inference path is guided by a new loss function to trade-off the SR accuracy and efficiency. Experiments demonstrate that, when working with three popular arbitrary-scale upsamplers, our TADT achieves state-of-the-art ASSR performance when compared with mainstream feature extractors, but with relatively fewer computational costs. The code will be publicly released. © 2024, CC BY.

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Evaluation of OSA Patient Sleep Stage Classification Performance Using a Multi-Channel PSG dataset

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Evaluation of OSA Patient Sleep Stage Classification Perform...

2022 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2022

作者： Na, Younghoon Kim, Dongyoung Kim, Dong-Kyu Lee, Jeong-Gun Division of Software Hallym University Chuncheon Korea Republic of Dept. of Computer Engineering Hallym University Chuncheon Korea Republic of Department of Otorhinolaryngology-Head and Neck Surgery Chuncheon Sacred Heart Hospital Hallym University College of Medicine Chuncheon Korea Republic of Institute of New Frontier Research Division of Big Data and Artificial Intelligence Chuncheon Sacred Heart Hospital Chuncheon Korea Republic of

ISBN: (数字)9781665464345

ISBN: (纸本)9781665464345

In this paper, we conduct a comparative analysis of sleep stage classification for patients having different levels of obstructive sleep apnea (OSA). For the analysis, we use 10 bio-signal channels: 4 EEG (Electroencephalogram) channels (F3-M2, F4-M1, C3-M2, and C4-M1), 2 EOG (Electrooculogram) channels (E1-M2 and E2-M1), and 4 other bio-channels (EMG, ECG, Flow, and Abdomen). In this work, in particular, we consider OSA severity for training and testing. Then, we investigated the detailed impacts of the OSA severity on the accuracy performance of a modern deep learning model with single channel and multiple channels. © 2022 IEEE.

关键词： Deep learning

Query-Centered Temporal Community Search via Time-Constrained Personalized PageRank

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arXiv 2023年

作者： Lin, Longlong Yuan, Pingpeng Li, Rong-Hua Zhu, Chunxue Qin, Hongchao Jin, Hai Jia, Tao College of Computer and Information Science Southwest University Chongqing400715 China National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technololgy Beijing Institute of Technology Beijing100081 China

Existing temporal community search suffers from two defects: (i) they ignore the temporal proximity between the query vertex q and other vertices but simply require the result to include q. Thus, they find many temporal irrelevant vertices (these vertices are called query-drifted vertices) to q for satisfying their cohesiveness, resulting in q being marginalized;(ii) their methods are NP-hard, incurring high costs for exact solutions or compromised qualities for approximate/heuristic algorithms. Inspired by these, we propose a novel problem named query-centered temporal community search to circumvent query-drifted vertices. Specifically, we first present a novel concept of Time-Constrained Personalized PageRank to characterize the temporal proximity between q and other vertices. Then, we introduce a model called β-temporal proximity core, which can combine temporal proximity and structural cohesiveness. Subsequently, our problem is formulated as an optimization task that finds a β-temporal proximity core with the largest β. To solve our problem, we first devise an exact and near-linear time greedy removing algorithm that iteratively removes unpromising vertices. To improve efficiency, we then design an approximate two-stage local search algorithm with bound-based pruning techniques. Finally, extensive experiments on eight real-life datasets and nine competitors show the superiority of the proposed solutions. © 2023, CC BY.

关键词： Iterative methods

Secure and Privacy-preserving data-sharing Framework based on Blockchain Technology for Al-Najaf/Iraq Oil Refinery 19

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Secure and Privacy-preserving Data-sharing Framework based o...

2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Umran, Samir M. Lu, SongFeng Abduljabbar, Zaid Ameen Lu, Zhi Feng, Bingyan Zheng, Lu Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Iraqi Cement State Company Ministry of Industry and Minerals Baghdad10011 Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China University of Basrah College of Education for Pure Sciences Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Industrial Internet Research Institute Wuhan Huazhong Numerical Control Co. Ltd Wuhan430074 China South-Central University for Nationalities College of Computer Science Wuhan430074 China

ISBN: (纸本)9798350346558

The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network;provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality. © 2022 IEEE.

关键词： Digital storage

Entropy-Based Model Generalization for Sleep Stage Classification 13

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Entropy-Based Model Generalization for Sleep Stage Classific...

13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Kim, Dongyoung Na, Younghoon Woo, Yunhee Kim, Dong-Kyu Lee, Jeong-Gun Hallym University Dept. of Computer Engineering Chuncheon Korea Republic of Hallym University Division of Software Chuncheon Korea Republic of Chuncheon Sacred Heart Hospital Hallym University College of Medicine Department of Otorhinolaryngology-Head and Neck Surgery Chuncheon Korea Republic of Institute of New Frontier Research and Division of Big Data and Artificial Intelligence Chuncheon Sacred Heart Hospital Chuncheon Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

The most recent researches on sleep stage classification have been focused on a model architecture to attain high classification accuracy. Sleep stage classification is performed by human sleep experts and consequently there are some inconsistencies of labels among the scoring experts. To improve robustness of the sleep stage classification model, it is essential to consider those noisy labels which have been commonly mentioned in typical image classification problems as well. To partly solve the problem, we employ 'entropy minimization' in a loss function. By including the entropy, we can make a generalized model with regard to the label noises. To validate the effectiveness of the use of entropy on model generalization, we use two different datasets gathered from two institutions. We train a model only using a dataset obtained from one institution. Then we test the model using the dataset of another institution to investigate the accuracy improvement by the generalization. © 2022 IEEE.

关键词： Entropy

Channel Estimation for Movable-Antenna MIMO Systems Via Tensor Decomposition

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arXiv 2024年

作者： Zhang, Ruoyu Cheng, Lei Zhang, Wei Guan, Xinrong Cai, Yueming Wu, Wen Zhang, Rui Ministry of Education School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen518055 China College of Communications Engineering Army Engineering University of PLA Nanjing210007 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore

In this letter, we investigate the channel estimation problem for MIMO wireless communication systems with movable antennas (MAs) at both the transmitter (Tx) and receiver (Rx). To achieve high channel estimation accuracy with low pilot training overhead, we propose a tensor decomposition-based method for estimating the parameters of multi-path channel components, including their azimuth and elevation angles, as well as complex gain coefficients, thereby reconstructing the wireless channel between any pair of Tx and Rx MA positions in the Tx and Rx regions. First, we introduce a two-stage Tx-Rx successive antenna movement pattern for pilot training, such that the received pilot signals in both stages can be expressed as a third-order tensor. Then, we obtain the factor matrices of the tensor via the canonical polyadic decomposition, and thereby estimate the angle/gain parameters for enabling the channel reconstruction between arbitrary Tx/Rx MA positions. In addition, we analyze the uniqueness condition of the tensor decomposition, which ensures the complete channel reconstruction between the whole Tx and Rx regions based on the channel measurements at only a finite number of Tx/Rx MA positions. Finally, simulation results are presented to evaluate the proposed tensor decomposition-based method as compared to existing methods, in terms of channel estimation accuracy and pilot overhead. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

Predicting Fall Events by a Spatio-Temporal Topological Network with Multiple Wearable Sensors

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Predicting Fall Events by a Spatio-Temporal Topological Netw...

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

作者： Xiaohu Li Jiawei Liu Guorui Liao Mingrui Yin Shu Wang Guoxin Su Jun Liao Li Liu School of Big Data & Software Engineering Chongqing University Chongqing China Chongqing Aerospace Rocket Electronic Technology Co. Ltd Chongqing China College of Computer Science & Technology Zhejiang University Zhejiang China School of Materials & Energy Southwest University Chongqing China School of Computing and Information Technology University of Wollongong NSW Australia

A key challenge in sensor-based fall prediction is the fact that a fall event can often occur in various configurations of fall poses together with their own spatio-temporal dependencies. This leads us to define a spatio-temporal model to explicitly characterize these internal configurations of poses. In particular, we introduce a graph neural network with spatio-temporal topological structure to encode such latent relations among poses by capturing representative patterns in fall events. Moreover, a human body orientation estimator is devised to capture human low limbs information, and as a result, separate pose dependencies are globally consistent. Empirical evaluations on two benchmark datasets and one in-house dataset suggest our approach significantly outperforms the state-of-the-art methods.

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An Improved Unbalanced data Classification Method Based on Hybrid Sampling Approach 4

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An Improved Unbalanced Data Classification Method Based on H...

2021 IEEE 4th International Conference on big data and Artificial Intelligence, BDAI 2021

作者： Xu, Biru Wang, Wenjia Yang, Rui Han, Qi University of Liverpool Department of Computer Science Liverpool United Kingdom School of Advanced Technology Research Institute of Big Data Analytics Xi an Jiaotong-Liverpool University Suzhou China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9781665412704

The problem of data imbalance has received far- reaching concerns since they could affect the accuracy of classification problem in the area of machine learning. As the minority class instances can be ignored by traditional classifiers, it is necessary to improve the recognition rate of minority instances. Therefore, the paper proposes a new hybrid sampling method to solve the data imbalance problem by enlarging the proportion of minority instances. For the oversampling part, a variant of SMOTE is provided combining methods of LR-SMOTE and CCR (Combined Cleaning and Resampling Algorithm);for the under-sampling part, the Tomek-link method is utilized to complete the task. After the pre-processing stage, the data set is classified by Random Forest (RF). Experimental results show that the novel algorithm effectively enhances the performance of RF on the data set with a higher accuracy. © 2021 IEEE.

关键词： data mining