Journals Articles Conferences News About CLP
Submit a paper Email Alert
Search
Search
Articles
Journals
News
Advanced Search
Top Searches
2D MaterialsTransformation opticsQuantum PhotonicsSmall lasersSemiconductor UV PhotonicsSupersymmetric microring laser arrays

Laser & Optoelectronics Progress, Volume. 61, Issue 12, 1237010(2024)

Multiscale Low-Light Image Enhancement Algorithm with Brightness Equalization and Edge Enhancement Algorithm

Lü Fu1,2, Xiangyan Cui1、*, and Tie Liu3,4
Author Affiliations
  • 1School of Software, Liaoning Technical University, Huludao 125105, Liaoning, China
  • 2Department of Basic Education, Liaoning Technical University, Huludao 125105, Liaoning, China
  • 3China Coal Technology & Engineering Group Shenyang Research Institute, Fushun 113122, Liaoning, China
  • 4State Key Laboratory of Coal Mine Safety Technology, Fushun 113122, Liaoning, China
    • show less
    AbstractGet PDF(in Chinese)
    Figures&Tables (10)
    Equations (19)
    References (30)
    Cited By (4)
    Tools
    Paper Information
    Topics
    Figures & Tables(10)
    Algorithm flowchart

    Fig. 1. Algorithm flowchart

    Download full size
    Edge enhancement image obtained by improved Sobel operator filter. (a) Original image; (b) traditional Sobel operator filter edge extraction image; (c) improved Sobel operator filter edge extraction image

    Fig. 2. Edge enhancement image obtained by improved Sobel operator filter. (a) Original image; (b) traditional Sobel operator filter edge extraction image; (c) improved Sobel operator filter edge extraction image

    Download full size
    Initial enhanced image. (a) Original image; (b) Retinex enhanced image; (c) improved Gamma correction image

    Fig. 3. Initial enhanced image. (a) Original image; (b) Retinex enhanced image; (c) improved Gamma correction image

    Download full size
    Image weight maps. (a) Laplacian weight map for Input1; (b) saliency weight map for Input1;(c) saturation weight map for Input1; (d) normalized weight map for Input1; (e) laplacian weight map for Input2; (f) saliency weight map for Input2; (g) saturation weight map for Input2; (h) normalized weight map for Input2

    Fig. 4. Image weight maps. (a) Laplacian weight map for Input1; (b) saliency weight map for Input1;(c) saturation weight map for Input1; (d) normalized weight map for Input1; (e) laplacian weight map for Input2; (f) saliency weight map for Input2; (g) saturation weight map for Input2; (h) normalized weight map for Input2

    Download full size
    Comparison image of each stage. (a) Original image; (b) initial enhanced image; (c) edge enhanced image; (c) edge enhanced image; (d) final enhanced image

    Fig. 5. Comparison image of each stage. (a) Original image; (b) initial enhanced image; (c) edge enhanced image; (c) edge enhanced image; (d) final enhanced image

    Download full size
    Examples of the LOL dataset. (a) Low-light images; (b) normal illumination images

    Fig. 6. Examples of the LOL dataset. (a) Low-light images; (b) normal illumination images

    Download full size
    Comparison results of LIME dataset. (a) Input images; (b) reference [11]; (c) reference [16]; (d) reference [18]; (e) reference [21]; (f) reference [22]; (g) proposed algorithm

    Fig. 7. Comparison results of LIME dataset. (a) Input images; (b) reference [11]; (c) reference [16]; (d) reference [18]; (e) reference [21]; (f) reference [22]; (g) proposed algorithm

    Download full size
    Comparison results of LOL dataset. (a) Input images; (b) reference [11]; (c) reference [16]; (d) reference [18]; (e) reference [21]; (f) reference [22]; (g) proposed algorithm; (h) original image

    Fig. 8. Comparison results of LOL dataset. (a) Input images; (b) reference [11]; (c) reference [16]; (d) reference [18]; (e) reference [21]; (f) reference [22]; (g) proposed algorithm; (h) original image

    Download full size

    • Table 1. Average values of indicators on LIME dataset

      View table

      Table 1. Average values of indicators on LIME dataset

      MethodCEIQ↑NIQE↓PIQE↓
      Reference［11］3.17815.557161.1746
      Reference［16］3.11216.136563.1891
      Reference［18］3.25835.510959.9104
      Reference［21］3.05885.962962.8006
      Reference［22］3.34425.552559.1297
      Proposed algorithm3.47685.115759.6703

    • Table 2. Average values of indicators on LOL dataset

      View table

      Table 2. Average values of indicators on LOL dataset

      MethodPSNR /dB↑SSIM↑CEIQ↑NIQE↓PIQE↓
      Reference［11］19.31140.71733.10999.120743.6867
      Reference［16］17.97780.75593.03799.187142.5455
      Reference［18］17.99310.72783.49438.749546.5681
      Reference［21］19.87240.76133.04319.654546.1735
      Reference［22］19.06420.72373.37878.828245.5896
      Proposed algorithm20.07050.79843.24597.519542.9064
    Tools

    Get Citation

    Copy Citation Text

    Lü Fu, Xiangyan Cui, Tie Liu. Multiscale Low-Light Image Enhancement Algorithm with Brightness Equalization and Edge Enhancement Algorithm[J]. Laser & Optoelectronics Progress, 2024, 61(12): 1237010

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Set citation alerts for article
    Save article for my favorites
    Paper Information

    Category: Digital Image Processing

    Received: Dec. 13, 2023

    Accepted: Feb. 18, 2024

    Published Online: Jun. 17, 2024

    The Author Email: Xiangyan Cui (1406248643@qq.com)

    DOI:10.3788/LOP232664

    CSTR:32186.14.LOP232664

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology