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
  • [21] A novel intelligent fault diagnosis method based on dual convolutional neural network with multi-level information fusion
    Dongying Han
    Jinghui Tian
    Peng Xue
    Peiming Shi
    Journal of Mechanical Science and Technology, 2021, 35 : 3331 - 3345
  • [22] Overlapping Community Hiding Method Based on Multi-Level Neighborhood Information
    Yang, Guoliang
    Wang, Yanwei
    Chang, Zhengchao
    Liu, Dong
    SYMMETRY-BASEL, 2022, 14 (11):
  • [23] A novel intelligent fault diagnosis method based on dual convolutional neural network with multi-level information fusion
    Han, Dongying
    Tian, Jinghui
    Xue, Peng
    Shi, Peiming
    JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, 2021, 35 (08) : 3331 - 3345
  • [24] Multi-level enhanced target identification fusion method
    Ku, JK
    Ock, SY
    SENSOR FUSION: ARCHITECTURES, ALGORITHMS, AND APPLICATIONS VI, 2002, 4731 : 188 - 195
  • [25] Multi-level difference information replenishment for medical image fusion
    Chen, Luping
    Wang, Xue
    Zhu, Ya
    Nie, Rencan
    APPLIED INTELLIGENCE, 2023, 53 (04) : 4579 - 4591
  • [26] Multi-level difference information replenishment for medical image fusion
    Luping Chen
    Xue Wang
    Ya Zhu
    Rencan Nie
    Applied Intelligence, 2023, 53 : 4579 - 4591
  • [27] Video quality enhancement based on visual attention model and multi-level exposure correction
    Guo-Shiang Lin
    Xian-Wei Ji
    Multimedia Tools and Applications, 2016, 75 : 9903 - 9925
  • [28] Video quality enhancement based on visual attention model and multi-level exposure correction
    Lin, Guo-Shiang
    Ji, Xian-Wei
    MULTIMEDIA TOOLS AND APPLICATIONS, 2016, 75 (16) : 9903 - 9925
  • [29] Using CNN with Multi-Level Information Fusion for Image Denoising
    Xie, Shaodong
    Song, Jiagang
    Hu, Yuxuan
    Zhang, Chengyuan
    Zhang, Shichao
    ELECTRONICS, 2023, 12 (09)
  • [30] An adaptive multi-level diagnostic system for video information fusion
    Han, J
    Liang, W
    Chang, SK
    MULTIMEDIA SYSTEMS AND APPLICATIONS-BOOK, 1999, 3528 : 327 - 338
12345