Exposure correction method based on multi-level pyramid information fusion

被引：0
|
作者：
Wu, Wenjiang [1 ,2 ]
Liu, Xinjun [1 ,3 ]
Zheng, Liaomo [1 ,2 ]
Wang, Shiyu [1 ,2 ]
Sun, Shujie [4 ]
机构：
[1] Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang,110168, China
[2] Shenyang CASNC Technology Co., Ltd., Shenyang,110168, China
[3] University of Chinese Academy of Sciences, Beijing,100049, China
[4] School of Electromechanical and Automotive Engineering, Yantai University, Yantai,264005, China
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2024年 / 30卷 / 10期
关键词：
To address underexposure and overexposure in images; a multi-level information fusion exposure correction network based on the Laplacian pyramid structure was developed. Each network level adopted a U-Net-like encoder-decoder architecture in its correction module. A multi-scale convolutional encoder based on ConvNeXt-tiny was designed as the primary feature extraction unit to enhance feature extraction ability while reducing the mod-el s parameter count. To tackle the issue of checkerboard artifacts arising during image up-sampling; a dual-path up-sampling module combining bilinear interpolation and sub-pixel convolution was proposed. The networkdemonstrated effective results in both quantitative and qualitative validations on a large-scale exposure correction dataset. Dowel positioning experiments showed significant improvements in feature repeatability; positioning accura- cy; and stability at varying contrast thresholds when the network was applied to image enhancement. © 2024 CIMS. All rights reserved;
D O I：
10.13196/j.cims.2023.0199
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：3578 / 3587
相关论文
共 50 条
  • [1] Multi-level pyramid fusion for efficient stereo matching
    Zhu, Jiaqi
    Li, Bin
    Zhao, Xinhua
    MULTIMEDIA SYSTEMS, 2024, 30 (05)
  • [2] Multi-level feature fusion pyramid network for object detection
    Guo, Zebin
    Shuai, Hui
    Liu, Guangcan
    Zhu, Yisheng
    Wang, Wenqing
    VISUAL COMPUTER, 2023, 39 (09): : 4267 - 4277
  • [3] Multi-level feature fusion pyramid network for object detection
    Zebin Guo
    Hui Shuai
    Guangcan Liu
    Yisheng Zhu
    Wenqing Wang
    The Visual Computer, 2023, 39 : 4267 - 4277
  • [4] A Micro-expression Recognition Method Based on Multi-level Information Fusion Network
    Chen, Yan
    Wu, Le-Chen
    Wang, Cong
    Zidonghua Xuebao/Acta Automatica Sinica, 2024, 50 (07): : 1445 - 1457
  • [5] Fault diagnosis based on fuzzy information multi-level fusion
    Meng, Xian-Yao
    Dai, Li-Xiong
    Dalian Haishi Daxue Xuebao/Journal of Dalian Maritime University, 2008, 34 (04): : 48 - 51
  • [6] Network security situation awareness method based on multi-source and multi-level information fusion
    Wen, Zhi-Cheng
    Chen, Zhi-Gang
    Deng, Xiao-Heng
    Liu, An-Feng
    Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University, 2015, 49 (08): : 1144 - 1152
  • [7] Multi-level data fusion method
    Lan, JH
    Ma, BH
    Zhou, ZY
    ISTM/2001: 4TH INTERNATIONAL SYMPOSIUM ON TEST AND MEASUREMENT, VOLS 1 AND 2, CONFERENCE PROCEEDINGS, 2001, : 235 - 238
  • [8] Multi-level fusion model and management method for satellite assembly information
    Wang, Zijun
    Wang, Xinxiang
    Wang, Yiquan
    Zhao, Peilin
    Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS, 2021, 27 (02): : 654 - 662
  • [9] Multi-level Fusion of Hard and Soft Information
    Biermann, Joachim
    Garcia, Jesus
    Krenc, Ksawery
    Nimier, Vincent
    Rein, Kellyn
    Snidaro, Lauro
    2014 17TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), 2014,
  • [10] Measures of information for multi-level data fusion
    Oxenham, MG
    Kewley, DJ
    Nelson, MJ
    SIGNAL PROCESSING, SENSOR FUSION, AND TARGET RECOGNITION V, 1996, 2755 : 271 - 282
12345