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. 59, Issue 14, 1415029(2022)

High Dynamic Range Imaging Method Based on YCbCr Color Space Fusion

Lu Tang1, Rongsheng Lu1、*, Yanqiong Shi2, and Haibing Hu1
Author Affiliations
  • 1College of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei 230009, Anhui , China
  • 2College of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei 230022, Anhui , China
    • show less
    AbstractGet PDF(in Chinese)
    Figures&Tables (12)
    Equations (35)
    References (24)
    Cited By (5)
    Tools
    Paper Information
    Topics
    Figures & Tables(12)
    Multi-resolution fusion algorithm flow based on YCbCr color space

    Fig. 1. Multi-resolution fusion algorithm flow based on YCbCr color space

    Download full size
    Y-channel multi-resolution weighted fusion process

    Fig. 2. Y-channel multi-resolution weighted fusion process

    Download full size
    Fusion results of different algorithms of “Venice” image sequence. (a) Input image sequence; (b) Mertens’ algorithm; (c) Guided Filtering; (d) Gradient Domain; (e) Adaptive Weights; (f) DSIFT-GF; (g) proposed algorithm

    Fig. 3. Fusion results of different algorithms of “Venice” image sequence. (a) Input image sequence; (b) Mertens’ algorithm; (c) Guided Filtering; (d) Gradient Domain; (e) Adaptive Weights; (f) DSIFT-GF; (g) proposed algorithm

    Download full size
    Fusion results of different algorithms of “Fountain” image sequence. (a) Input image sequence; (b) Mertens’ algorithm; (c) Guided Filtering; (d) Gradient Domain; (e) Adaptive Weights; (f) DSIFT-GF; (g) proposed algorithm

    Fig. 4. Fusion results of different algorithms of “Fountain” image sequence. (a) Input image sequence; (b) Mertens’ algorithm; (c) Guided Filtering; (d) Gradient Domain; (e) Adaptive Weights; (f) DSIFT-GF; (g) proposed algorithm

    Download full size
    Fusion results of different algorithms of “Outdoor” image sequence. (a) Input image sequence; (b) Mertens’ algorithm; (c) Guided Filtering; (d) Gradient Domain; (e) Adaptive Weights; (f) DSIFT-GF; (g) proposed algorithm

    Fig. 5. Fusion results of different algorithms of “Outdoor” image sequence. (a) Input image sequence; (b) Mertens’ algorithm; (c) Guided Filtering; (d) Gradient Domain; (e) Adaptive Weights; (f) DSIFT-GF; (g) proposed algorithm

    Download full size
    Fusion results of different algorithms of “Indoor scene” image sequence. (a) Input image sequence; (b) Mertens’ algorithm; (c) Guided Filtering; (d) Gradient Domain; (e) Adaptive Weights; (f) DSIFT-GF; (g) proposed algorithm

    Fig. 6. Fusion results of different algorithms of “Indoor scene” image sequence. (a) Input image sequence; (b) Mertens’ algorithm; (c) Guided Filtering; (d) Gradient Domain; (e) Adaptive Weights; (f) DSIFT-GF; (g) proposed algorithm

    Download full size
    Fusion results of different algorithms of “Outdoor scene” image sequence. (a) Input image sequence; (b) Mertens’ algorithm; (c) Guided Filtering; (d) Gradient Domain; (e) Adaptive Weights; (f) DSIFT-GF; (g) proposed algorithm

    Fig. 7. Fusion results of different algorithms of “Outdoor scene” image sequence. (a) Input image sequence; (b) Mertens’ algorithm; (c) Guided Filtering; (d) Gradient Domain; (e) Adaptive Weights; (f) DSIFT-GF; (g) proposed algorithm

    Download full size
    Fusion results of focus stacking image sequence. (a) Input image sequence; (b) Mertens’ algorithm; (c) proposed algorithm

    Fig. 8. Fusion results of focus stacking image sequence. (a) Input image sequence; (b) Mertens’ algorithm; (c) proposed algorithm

    Download full size
    Objective evaluation of multi-exposure images fusion results

    Fig. 9. Objective evaluation of multi-exposure images fusion results

    Download full size
    Objective evaluation graph of fusion results of focus stacking image

    Fig. 10. Objective evaluation graph of fusion results of focus stacking image

    Download full size

    • Table 1. Subjective scoring criteria

      View table

      Table 1. Subjective scoring criteria

      ScoreQuality scaleHinder scale
      1Very badHinder viewing very seriously
      2BadHinder viewing
      3GeneralCan clearly see the change of image quality and hinder viewing very slightly
      4Good

      Can see the change of image quality but does not hinder viewing

      No picture quality change at all

      5Very Good

    • Table 2. Objective evaluation table of fusion results of focus stacking image

      View table

      Table 2. Objective evaluation table of fusion results of focus stacking image

      Objective evaluation indexMertens’ algorithmProposed algorithm
      Standard deviation33.565957.3815
      Average gradient1.06914.7621
      Information entropy6.38027.2109
      Spatial frequency2.423610.3741
    Tools

    Get Citation

    Copy Citation Text

    Lu Tang, Rongsheng Lu, Yanqiong Shi, Haibing Hu. High Dynamic Range Imaging Method Based on YCbCr Color Space Fusion[J]. Laser & Optoelectronics Progress, 2022, 59(14): 1415029

    Download Citation

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

    Category: Machine Vision

    Received: May. 19, 2021

    Accepted: Jun. 10, 2021

    Published Online: Jul. 1, 2022

    The Author Email: Rongsheng Lu (rslu@hfut.edu.cn)

    DOI:10.3788/LOP202259.1415029

    Topics

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