云南高校图书馆联盟文献共享服务平台- LOW-LIGHT

LOW-LIGHT: 作品数：38被引量：57H指数：5; 导出分析报告; 相关领域：自动化与计算机技术电子电信更多>>; 相关作者：邹正峰芦汉生白廷柱高稚允更多>>; 相关机构：北京理工大学更多>>; 相关期刊：《Optoelectronics Letters》《The Journal of China Universities of Posts and Telecommunications》《Automotive Innovation》《Horticulture Research》更多>>; 相关基金：国家自然科学基金北京市自然科学基金国家重点基础研究发展计划中国博士后科学基金更多>>

LLE-Fuse:Lightweight Infrared and Visible Light Image Fusion Based on Low-Light Image Enhancement: 《Computers, Materials & Continua》2025年第3期4069-4091,共23页Song Qian Guzailinuer Yiming Ping Li Junfei Yang Yan Xue Shuping Zhang; This researchwas Sponsored by Xinjiang Uygur Autonomous Region Tianshan Talent Programme Project(2023TCLJ02);Natural Science Foundation of Xinjiang Uygur Autonomous Region(2022D01C349).; Infrared and visible light image fusion technology integrates feature information from two different modalities into a fused image to obtain more comprehensive information.However,in low-light scenarios,the illuminati...; 关键词：Infrared images image fusion low-light enhancement feature extraction computational resource optimization

Local Content-Aware Enhancement for Low-Light Images with Non-Uniform Illumination: 《Computers, Materials & Continua》2025年第3期4669-4690,共22页Qi Mu Yuanjie Guo Xiangfu Ge Xinyue Wang Zhanli Li; In low-light image enhancement,prevailing Retinex-based methods often struggle with precise illumina-tion estimation and brightness modulation.This can result in issues such as halo artifacts,blurred edges,and diminis...; 关键词：RETINEX non-uniform low illumination local content-aware effective guided image filtering

A Transformer Network Combing CBAM for Low-Light Image Enhancement: 《Computers, Materials & Continua》2025年第3期5205-5220,共16页Zhefeng Sun Chen Wang; Recently,a multitude of techniques that fuse deep learning with Retinex theory have been utilized in the field of low-light image enhancement,yielding remarkable outcomes.Due to the intricate nature of imaging scenari...; 关键词：Low-light image enhancement CBAM TRANSFORMER

Retinexformer+:Retinex-Based Dual-Channel Transformer for Low-Light Image Enhancement: 《Computers, Materials & Continua》2025年第2期1969-1984,共16页Song Liu Hongying Zhang Xue Li Xi Yang; supported by the Key Laboratory of Forensic Science and Technology at College of Sichuan Province(2023YB04).; Enhancing low-light images with color distortion and uneven multi-light source distribution presents challenges. Most advanced methods for low-light image enhancement are based on the Retinex model using deep learning...; 关键词：Low-light image enhancement RETINEX transformer model

Unsupervised Low-Light Image Enhancement Based on Explicit Denoising and Knowledge Distillation: 《Computers, Materials & Continua》2025年第2期2537-2554,共18页Wenkai Zhang Hao Zhang Xianming Liu Xiaoyu Guo Xinzhe Wang Shuiwang Li; support by the Guangxi Natural Science Foundation(Grant No.2024GXNSFAA010484);the NationalNatural Science Foundation of China(No.62466013),this work has been made possible.; Under low-illumination conditions, the quality of image signals deteriorates significantly, typically characterized by a peak signal-to-noise ratio (PSNR) below 10 dB, which severely limits the usability of the images...; 关键词：Deep learning low-light image enhancement real-time processing knowledge distillation

Neural Network-Powered License Plate Recognition System Design: 《Engineering（科研）》2024年第9期284-300,共17页Sakib Hasan Md Nagib Mahfuz Sunny Abdullah Al Nahian Mohammad Yasin; The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The ...; 关键词：Intelligent Traffic Control Systems Automatic License Plate Recognition (ALPR) Neural Networks Vehicle Surveillance Traffic Management License Plate Recognition Algorithms Image Extraction Character Segmentation Character Recognition Low-Light Environments Inclement Weather Empirical Findings Algorithm Accuracy Simulation Outcomes DIGITALIZATION

Low‑light enhancement method with dual branch feature fusion and learnable regularized attention: 《Frontiers of Optoelectronics》2024年第3期93-111,共19页Yixiang Sun Mengyao Ni Ming Zhao Zhenyu Yang Yuanlong Peng Danhua Cao; supported by State Grid Corporation of China(5700-202325308A-1-1-ZN);Information&Telecommunication Branch of State Grid Jiangxi Electric Power Company.; Restricted by the lighting conditions,the images captured at night tend to sufer from color aberration,noise,and other unfavorable factors,making it difcult for subsequent vision-based applications.To solve this probl...; 关键词：Power inspection Low-light enhancement Feature fusion Learnable regularized attention

Highly Differentiated Target Detection under Extremely Low-Light Conditions Based on Improved YOLOX Model: 《Computer Modeling in Engineering & Sciences》2024年第8期1507-1537,共31页Haijian Shao Suqin Lei Chenxu Yan Xing Deng Yunsong Qi; supported by National Sciences Foundation of China Grants(No.61902158).; This paper expounds upon a novel target detection methodology distinguished by its elevated discriminatory efficacy,specifically tailored for environments characterized by markedly low luminance levels.Conventional me...; 关键词：Target detection extremely low-light wavelet transformation highly differentiated features YOLOX

LLTH‑YOLOv5:A Real‑Time Traffic Sign Detection Algorithm for Low‑Light Scenes被引量：3: 《Automotive Innovation》2024年第1期121-137,共17页Xiaoqiang Sun Kuankuan Liu Long Chen Yingfeng Cai Hai Wang; National Natural Science Foundation of China,U20A20331,Long Chen.; Traffic sign detection is a crucial task for autonomous driving systems.However,the performance of deep learning-based algorithms for traffic sign detection is highly affected by the illumination conditions of scenari...; 关键词：Deep learning Traffic sign detection Low-light enhancement YOLOv5 Object detection

More Than Lightening:A Self-Supervised Low-Light Image Enhancement Method Capable for Multiple Degradations: 《IEEE/CAA Journal of Automatica Sinica》2024年第3期622-637,共16页Han Xu Jiayi Ma Yixuan Yuan Hao Zhang Xin Tian Xiaojie Guo; supported by the National Natural Science Foundation of China(62276192)。; Low-light images suffer from low quality due to poor lighting conditions,noise pollution,and improper settings of cameras.To enhance low-light images,most existing methods rely on normal-light images for guidance but ...; 关键词：Color correction low-light image enhancement self-supervised learning.

LOW-LIGHT