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High-density, nonvolatile SRAM using monolithic 3D integration of InGaZnO thin-film transistors and Hf 0.5 Zr 0.5 O 2 -based ferroelectric capacitors

Device

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Device 2025年

作者： Qihan Liu Yu Li Liwei Liu Hao Jiang Haozhe Zhu Zexing Chen Yang Yang Mengwei Si Can Li Peng Lin Yingfen Wei Qi Liu State Key Laboratory of Integrated Chips and Systems Frontier Institute of Chip and System Fudan University Shanghai China Institute of Microelectronics of the Chinese Academy of Sciences Beijing China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong China College of Computer Science and Technology Zhejiang University Hangzhou China

Monolithic three-dimensional (M3D) integration of back-end-of-line (BEOL)-compatible and high-performance transistors and memory devices offer a promising strategy to overcome the limitations of conventional silicon-based computing techniques. Specifically, static random-access memory (SRAM), while valued for speed and endurance, faces the challenges of a large footprint and static power consumption. Here, we present a four-transistor-two-capacitor (4T2C) nonvolatile SRAM (nvSRAM) utilizing vertically stacked indium gallium zinc oxide (IGZO) thin-film transistors (TFTs) and Hf 0.5 Zr 0.5 O 2 (HZO)-based ferroelectric capacitors (FeCaps) under a BEOL-compatible thermal budget. The HZO FeCaps and IGZO TFTs are optimized toward their M3D integration with high performance and inter-tier uniformity. Our 3-tier M3D-integrated 4T2C nvSRAM achieves a 2-transistor (2T) footprint and data retention at 85°C for 10 5 s after power off. Moreover, design space and optimization strategies of the 4T2C nvSRAM are explored for higher operation speed, lower energy consumption, and more robust data stability.

关键词： monolithic 3D integration back-end-of-line nonvolatile SRAM ferroelectric capacitor IGZO transistor emerging memory DTI-2: Explore

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Dynamic Resilient Graph Games for State-Dependent Jamming Attacks Analysis on Multi-Agent systems

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IFAC-PapersOnLine 2020年第2期53卷 3421-3426页

作者： Yurid Nugraha Ahmet Cetinkaya Tomohisa Hayakawa Hideaki Ishii Quanyan Zhu Dept. of Systems and Control Engineering Tokyo Institute of Technology Tokyo 152-8552 Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo 101-8430 Japan Dept. of Computer Science Tokyo Insitute of Technology Yokohama 226-8502 Japan Dept. of Electrical and Computer Engineering New York University Brooklyn NY 11201 USA

A cybersecurity problem for a multi-agent consensus problem is investigated through a dynamic game formulation. Specifically, we consider a game repeatedly played between a jamming attacker and a defender. The attacker attempts to jam the links between a number of agents to delay their consensus. On the other hand, the defender tries to maintain the connection between agents by attempting to recover some of the jammed links with the goal of achieving faster consensus. In each game, the players decide which links to attack/recover and for how long to continue doing so based on a Lyapunov-like function representing the largest difference between the states of the agents. We analyze the subgame perfect equilibrium of the game and obtain an upper bound of the consensus time that is influenced by the strategies of the players. The results are illustrated with a numerical example.

关键词： Multi-agent systems Consensus problem Game theory Cybersecurity

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Resistive memory-based zero-shot liquid state machine for multimodal event data learning

arXiv

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arXiv 2023年

作者： Lin, Ning Wang, Shaocong Li, Yi Wang, Bo Shi, Shuhui He, Yangu Zhang, Woyu Yu, Yifei Zhang, Yue Zhang, Xinyuan Wong, Kwunhang Wang, Songqi Chen, Xiaoming Jiang, Hao Zhang, Xumeng Lin, Peng Xu, Xiaoxin Qi, Xiaojuan Wang, Zhongrui Shang, Dashan Liu, Qi Liu, Ming Department of Electrical and Electronic Engineering the University of Hong Kong Hong Kong Key Lab of Fabrication Technologies for Integrated Circuits and Key Laoratory of Microelectronic Devices and Integrated Technology Institute of Microelectronics the Chinese Academy of Sciences Beijing100029 China The School of Microelectronics Southern University of Science and Technology Shenzhen518055 China ACCESS – AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong University of Chinese Academy of Sciences Beijing100049 China Frontier Institute of Chip and System Fudan University Shanghai200433 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

The human brain is a complex spiking neural network (SNN), capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains minimal power consumption through event-based signal propagation. However, replicating the human brain in neuromorphic hardware presents both hardware and software challenges. Hardware limitations, such as the slowdown of Moore’s law and Von Neumann bottleneck, hinder the efficiency of digital computers. Additionally, SNNs are characterized by their software training complexities. To this end, we propose a hardware-software co-design on a 40 nm 256 Kb in-memory computing macro that physically integrates a fixed and random liquid state machine (LSM) SNN encoder with trainable artificial neural network (ANN) projections. We showcase the zero-shot LSM-based learning of multimodal events on the N-MNIST and N-TIDIGITS datasets, including visual and audio data association, as well as neural and visual data alignment for brain-machine interfaces. Our co-design achieves classification accuracy comparable to fully optimized software models, resulting in a 152.83 and 393.07-fold reduction in training costs compared to SOTA contrastive language-image pre-training (CLIP) and Prototypical networks, and a 23.34 and 160-fold improvement in energy efficiency compared to cutting-edge digital hardware, respectively. These proof-of-principle prototypes demonstrate zero-shot multimodal events learning capability for emerging efficient and compact neuromorphic hardware. © 2023, CC BY-NC-SA.

关键词： Image enhancement

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SYNAPSE: An international roadmap to large brain imaging

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Physics Reports 2023年第1期999卷 1-60页

作者： Stampfl, Anton P.J. Liu, Zhongdong Hu, Jun Sawada, Kei Takano, H. Kohmura, Yoshiki Ishikawa, Tetsuya Lim, Jae-Hong Je, Jung-Ho Low, Chian-Ming Teo, Alvin Tok, Eng Soon Tan, Tin Wee Ban, Kenneth Libedinsky, Camilo Tan, Francis Chee Kuan Chen, Kuan-Peng Yang, An-Cheng Chuang, Chao-Chun Chen, Nan-Yow Shih, Chi-Tin Lee, Ting-Kuo Yang, De-Nian Lai, Hsu-Chao Shuai, Hong-Han Cheng, Chang-Chieh Ching, Yu-Tai Li, Chia-Wei Charng, Ching-Che Lo, Chung-Chuan Chiang, Ann-Shyn Recur, Benoit Petibois, Cyril Cheng, Chia-Liang Chen, Hsiang-Hsin Yang, Shun-Min Hwu, Yeukuang Rojviriya, Catleya Rugmai, Supagorn Rujirawat, Saroj Margaritondo, Giorgio Australian Nuclear Science and Technology Organization Lucas Heights 2234 NSW Australia Henan university of Technology Zhengzhou China Shanghai Institute of Applied Physics Chinese Academy of Science Shanghai China RIKEN SPring-8 Center Hyogo 679-5148 Japan Pohang Accelerator Laboratory Pohang University of Science and Technology Pohang Gyeongbuk 37673 South Korea Department of Materials Science and Engineering Pohang University of Science and Technology Gyeongbuk Pohang 37673 South Korea Department of Pharmacology Department of Anaesthesia and Healthy Longevity Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore School of Chemical and Life Sciences Nanyang Polytechnic Singapore eMaGIC-Lab Department of Physics National University of Singapore Singapore National Supercomputing Center Singapore Department of Psychology The N1 Institute for Health National University of Singapore Singapore Institute of Molecular and Cell Biology A*STAR Singapore Department of Anaesthesia National University Hospital National University Health System Singapore National Center for High-Performance Computing Hsinchu Taiwan Department of Physics Tunghai University Taichung Taiwan Department of Physics National Tsing Hua University Hsinchu Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Information Technology Service Center National Yang Ming Chiao Tung University Hsinchu Taiwan National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Life Science National Tsing Hua University Hsinchu Taiwan Institute of Systems Neuroscience and Brain Research Center National Tsing Hua University Hsinchu Taiwan Institute of Physics Academia Sinica Taipei Taiwan Department of Physics National Dong Hwa University

Since 2020, synchrotron radiation facilities in several Asia-Pacific countries have been collaborating in a major project called “SYNAPSE” (Synchrotrons for Neuroscience: an Asia-Pacific Scientific Enterprise). They use x-ray imaging to attack in a coordinated fashion one of the major issues in modern science: the structure of animal and human brains, including neurons and connections. The objective is to develop Google-like maps also including detailed structural and functional information for selected regions of interest. The sheer mass of data needed for the objective poses huge problems for the acquisition, processing, storage and use of images. In order to complete the task within a reasonable time, the key element of the SYNAPSE strategy is the parallel and coordinated work of several facilities on the same specimens. This article reviews different aspects of the enterprise, including the foundations of synchrotron radiation, coherence and of its role in advanced imaging, electron accelerators, x-ray optics and detectors. This will provide the foundation for an extensive presentation of the different components of SYNAPSE, with an overview of results already obtained within the consortium. © 2022

关键词： Brain mapping Phase contrast Radiology Supercomputing Synchrotron X-rays

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Trust-based core social graph convolution: An innovative framework for location recommendation

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Expert systems with Applications 2025年 285卷

作者： Xie, Tianyu Chen, Yunliang Wu, Yong Cui, Ningning Chen, Haofeng Lu, Xuanyu Huang, Xiaohui Wang, Yuewei Li, Jianxin School of Computer Science China University of Geosciences Wuhan Hubei430074 China Hardware and Artificial Intelligence Lab Institute of Computer Engineering Heidelberg University Heidelberg69120 Germany School of Data Science and Artificial Intelligence Chang'an University Xi'an710064 China System Design Institute of Hubei Aerospace Technology Academy Wuhan430040 China Discipline of Business Systems and Operations School of Business and Law Edith Cowan University Australia

With the increasing popularity of location-based social networks (LBSNs), recommendation based on LBSNs has attracted wide attention in academic and industrial domains. Traditional recommendation systems face critical challenges in handling data sparsity and underutilized social trust. Existing traditional models particularly struggle to incorporate both trust-level differences and implicit relationships effectively within large-scale personalized recommendations. This limitation directly leads to aggravated data sparsity, further exacerbating cold-start scenarios and significantly reducing recommendation accuracy for long-tail items. Existing widely-adopted deep learning models, such as graph convolutional networks (GCNs), apply equal propagation weights to all neighbor nodes, which not only causes feature convergence between high-trust and ordinary users (over-smoothing) but also incurs substantial computational overhead. We propose the Trust-Based Core Graph Collaborative Filtering (TCGCF) framework, integrating trust-weighted core graph analysis with trust-constrained graph convolution. TCGCF captures both explicit and implicit trust relationships, enhances personalization, and mitigates over-smoothing. Our trust-weighted core graph analysis identifies influential users in information propagation, while the trust-constrained convolution scheme enables precise, differentiated information flow. Experiments on real-world datasets demonstrate that TCGCF improves recommendation accuracy and computational efficiency, outperforming existing models in precision, recall, and suitability for large-scale applications. © 2025

关键词： Collaborative filtering

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Meet the challenges in the “Carbon Age”

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Carbon Research 2022年第1期1卷 1-2页

作者： Wu, Fengchang Li, Fangbai Zhao, Xiaoli Bolan, Nanthi S. Fu, Pingqing Lam, Su Shiung Mašek, Ondřej Ong, Hwai Chyuan Pan, Bo Qiu, Xueqing Rinklebe, Jörg Tsang, Daniel C. W. Van Zwieten, Lukas Vithanage, Meththika Wang, Shaobin Xing, Baoshan Zhang, Gan Wang, Hailong State Key Laboratory of Environmental Criteria and Risk Assessment Chinese Research Academy of Environmental Sciences Beijing China Institute of Eco-environmental and Soil Sciences Guangdong Academy of Sciences Guangzhou China School of Agriculture and Environment The University of Western Australia Perth Australia Institute of Surface-Earth System Science School of Earth System Science Tianjin University Tianjin China Higher Institution Centre of Excellence (HICoE) Institute of Tropical Aquaculture and Fisheries (AKUATROP) Universiti Malaysia Terengganu Kuala Nerus Malaysia School of GeoSciences University of Edinburgh Edinburgh UK School of Information Systems and Modelling University of Technology Sydney Sydney Australia Faculty of Environmental Science & Engineering Kunming University of Science & Technology Kunming China School of Chemical Engineering and Light Industry Guangdong University of Technology Guangzhou China School of Architecture and Civil Engineering University of Wuppertal Wuppertal Germany Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Kowloon Hong Kong China New South Wales Department of Primary Industries Wollongbar Australia Ecosphere Resilience Research Center University of Sri Jayewardenepura Nugegoda Sri Lanka School of Chemical Engineering and Advanced Materials University of Adelaide Adelaide Australia Stockbridge School of Agriculture University of Massachusetts Amherst USA Guangzhou Institute of Geochemistry Chinese Academy of Sciences Guangzhou China School of Environmental and Chemical Engineering Foshan University Foshan China

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The Bankable Economic Potential of Utility-Scale Solar Pv: A Novel Gis-Based Framework with Application to Indonesia

SSRN

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SSRN 2023年

作者： Langer, Jannis Ashqar, Ziad Zhou, Yilong Isabella, Olindo Praktiknjo, Aaron Quist, Jaco Kwee, Zenlin Blok, Kornelis Delft University of Technology Faculty of Technology Policy and Management Department of Engineering Systems and Services Jaffalaan 5 Delft2628 BX Netherlands Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Department of Electrical Sustainable Energy Photovoltaic Materials and Devices group Mekelweg 4 Delft2628 CD Netherlands RWTH Aachen University Energy System Economics Institute for Future Energy Consumer Needs and Behavior E.ON Energy Research Center Mathieustraße 10 Aachen52074 Germany Delft University of Technology Faculty of Technology Policy and Management Department of Values Technology and Innovation Jaffalaan 5 Delft2628 BX Netherlands

Geospatial analysis is useful to calculate and map solar photovoltaic’s (PV) economic potential. However, recent studies do not address PV’s bankable potential for practical implementation and show limitations in (1) potential and input reporting, (2) studied metrics, (3) uncertainty analysis, and (4) policy analysis. To tackle these limitations, we propose a novel framework for utility-scale PV’s bankable potential, which combines geospatial analysis and project finance. The framework involves 20 years of hourly solar electricity production, a debt sizing and cash flow analysis model, and Monte Carlo simulation. We report a conservative technical potential of 9.3 TWpeak and 12.1 PWh/year in Indonesia. Considering expected 2030 demand, PV’s bankable potential is up to 40.8 TWh/year mostly situated in East Indonesia where population and demand are low. PV’s bankability is currently limited by strict loan maturities and low electricity tariffs, and could be improved to up to 232 TWh/year (52% of 2030 demand) via a feed-in tariff of 11.5 US¢(2021)/kWh and a temporary lift of local content for solar panels. Despite their effectiveness, policymakers should be aware of possible side effects of PV policies, e.g. for local coal-based industries and rural communities with limited electricity access. © 2023, The Authors. All rights reserved.

关键词： Economic analysis

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Prediction of Soil Wind Erodibility Using Rfe-Based Ensemble Modelling

SSRN

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SSRN 2022年

作者： Roy, Bishwajit Malviya, Lokesh Mal, Sandip Kumar, Amrendra Kumar, Radhikesh Jahed Armaghani, Danial Kumar, Deepak Banerjee, Avishek School of Computer Science UPES Uttarakhand Dehradun248007 India School of SCSE VIT MP Bhopal466114 India Dept.of CE RIT UK Roorkee247667 India Dept. of CSE NIT Bihar Patna800001 India Department of Urban Planning Engineering Networks and Systems Institute of Architecture and Construction South Ural State Universit 76 Lenin Pros ect Chelyabinsk454080 Russia Dept. of CE NIT Bihar Patna800001 India Department of CSBS Asansol Engineering College Asansol713305 India

Due to the intricate nature of erodibility, sensitivity of soil particles and limited amount of data, the consequent modelling of soil wind erodibility is a challenging task for researchers. Therefore, the robust prediction of soil wind erodibility along with the sensitive factors is often an indispensable task. This study analyses portable wind tunnel-based measured wind erosion rate (gm −2 s −1 ) from 118 sites in Kerman Province, southeast Iran where 17 different predictors were considered for each site. This paper investigates the importance of a set of predictors which are most affected by soil erosion rate. Furthermore, this study proposes a novel an optimum feature selection-based heterogeneous ensemble meta-estimator (voting ensemble (VE)) for predicting wind erosion rate. A linear regression-based recursive feature elimination technique with 2-fold cross validation is used to identify the 7significant important predictors, called i) VFS (very fine sand), ii) CS (coarse sand), iii) VCS (very coarse sand), iv) GC (gravel coverage), v) MWD (mean weight diameter), vi) surface crust, and vii) moisture, that affects the soil erodibility rate. Then, a VE with 3 unfitted estimators is proposed, which averages the predictions of singleton estimators to enhance the prediction accuracy. The 3 singleton unfitted estimators, called support vector machine (linear kernel), random forest and gradient boosting machine, are used with grid search approach. The investigation identifies the surface crust as the most effective parameter in predicting soil erosion rate. © 2022, The Authors. All rights reserved.

关键词： Soils

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Anisotropic tensile properties and high-cycle fatigue behavior of Ti6Al4V fabricated by single and dual laser-powder bed fusion

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Materials Science and engineering: A 2025年 941卷

作者： Guotai Li Peichen Hu Yuzhan Fan Ming Wu Mingjun Chen Tianyu Yu State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin Heilongjiang 150001 China Key Laboratory of Micro-systems and Micro-structures Manufacturing Ministry of Education Harbin Heilongjiang 150001 China Department of Computer Science KU Leuven 3001 Leuven Belgium Department of Mechanical Engineering KU Leuven 3001 Leuven Belgium

The development of multi laser-powder bed fusion (PBF) technology has significantly reduced the production time for large engine components. However, the impact of thermal effects and microstructural characteristics in the laser beam overlap region on the anisotropic mechanical behavior of metallic materials remains unclear. This study investigated the anisotropic tensile properties and high-cycle fatigue behavior of Ti6Al4V produced by both single laser-PBF (SL-PBF) and dual laser-PBF (DL-PBF). As-built DL-PBF samples exhibited high strength (1404 MPa) but low ductility (3.6 %). After heat treatment, the samples achieved a strength-ductility balance, with an ultimate tensile strength of 1089 MPa, and an elongation of 17.1 %. The mechanical properties of Ti6Al4V produced by DL-PBF were influenced by the α lath thickness, dislocation density and texture. The precipitation of small-angle grain boundaries during DL-PBF process was found to contribute to the strong anisotropy. Due to the influence of internal pore forming, the fatigue strength of the DL-PBF samples was lower than that of the SL-PBF samples. Pores in the dual-laser beam overlap region accelerated the fatigue crack propagation, resulting in a venation-like pattern. In contrast, cracks in the SL-PBF samples propagated in a circular pattern around internal pore. A high-speed camera was employed to capture the dual-laser beams dynamics during pore formation. This study provides a comprehensive analysis of the mechanical properties and pore formation mechanisms in the DL-PBF process.

关键词： Anisotropy Dual laser-powder bed fusion High-cycle fatigue Tensile properties Ti6Al4V

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NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

arXiv

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arXiv 2024年

作者： Liu, Xiaoning Wu, Zongwei Li, Ao Vasluianu, Florin Zhang, Yulun Gu, Shuhang Zhang, Le Zhu, Ce Timofte, Radu Jin, Zhi Wu, Hongjun Wang, Chenxi Ling, Haitao Cai, Yuanhao Bian, Hao Zheng, Yuxin Lin, Jing Yuille, Alan Shao, Ben Guo, Jin Liu, Tianli Wu, Mohao Feng, Yixu Hou, Shuo Lin, Haotian Zhu, Yu Wu, Peng Dong, Wei Sun, Jinqiu Zhang, Yanning Yan, Qingsen Zou, Wenbin Yang, Weipeng Li, Yunxiang Wei, Qiaomu Ye, Tian Chen, Sixiang Zhang, Zhao Zhao, Suiyi Wang, Bo Luo, Yan Zuo, Zhichao Wang, Mingshen Wang, Junhu Wei, Yanyan Sun, Xiaopeng Gao, Yu Huang, Jiancheng Chen, Hongming Chen, Xiang Tang, Hui Chen, Yuanbin Zhou, Yuanbo Dai, Xinwei Qiu, Xintao Deng, Wei Gao, Qinquan Tong, Tong Li, Mingjia Hu, Jin He, Xinyu Guo, Xiaojie Sabarinathan Uma, K. Sasithradevi, A. Sathya Bama, B. Mohamed Mansoor Roomi, S. Srivatsav, V. Wang, Jinjuan Sun, Long Chen, Qiuying Shao, Jiahong Zhang, Yizhi Conde, Marcos V. Feijoo, Daniel Benito, Juan C. García, Alvaro Lee, Jaeho Kim, Seongwan Sharif, S.M.A. Khujaev, Nodirkhuja Tsoy, Roman Murtaza, Ali Khairuddin, Uswah Faudzi, Ahmad'Athif Mohd Malagi, Sampada Joshi, Amogh Akalwadi, Nikhil Desai, Chaitra Tabib, Ramesh Ashok Mudenagudi, Uma Lian, Wenyi Lian, Wenjing Kalyanshetti, Jagadeesh Aralikatti, Vijayalaxmi Ashok Yashaswini, Palani Upasi, Nitish Hegde, Dikshit Patil, Ujwala Sujata, C. Yan, Xingzhuo Hao, Wei Fu, Minghan Choksy, Pooja Sarvaiya, Anjali Upla, Kishor Raja, Kiran Yan, Hailong Zhang, Yunkai Li, Baiang Zhang, Jingyi Zheng, Huan University of Electronic Science and Technology of China China Computer Vision Lab University of Würzburg Germany Shanghai Jiao Tong University China Computer Vision Lab ETH Zurich Switzerland School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen Campus Guangdong Shenzhen518107 China Guangdong Provincial Key Laboratory of Fire Science and Technology Guangzhou510006 China Johns Hopkins University United States Tsinghua University China Zhejiang Dahua Technology Co. Ltd. China School of Computer Science Northwestern Polytechnical University China School of Software Northwestern Polytechnical University China School of Computer Science Xi'an University of Architecture and Technology China South China University of Technology China Fuzhou University China Chengdu University of Information Technology China Hong Kong University of Science and Technology Guangzhou China Hefei University of Technology China Individual Researcher Shenyang Aerospace University China Nanjing University of Science and Technology China Fuzhou University Fuzhou China Imperial Vision Technology Fuzhou China Tianjin University China Couger Inc Japan Sasi Institute of Technology & Engineering India Vellore Institute of Technology India Thiagarajar college of engineering India Coventry University United Kingdom Cidaut AI CVLab University of Wuerzburg Germany Opt-AI University Teknologi Malaysia Kuala Lumpur Malaysia Universiti Teknologi Malaysia Kuala Lumpur Malaysia KLE Technological University Karnataka Hubballi India School of Electronics and Communication Engineering KLE Technological University Karnataka Hubballi India School of Computer Science and Engineering KLE Technological University Karnataka Hubballi India Uppsala University Sweden Northeastern University United States Bosch Investment Ltd. China Fortinet Inc. University of Saskatchewan Canada Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technolog

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and beyond), non-uniform illumination, backlighting, extreme darkness, and night scenes. A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions. This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field. © 2024, CC BY.

关键词： Image enhancement

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