• Laser & Optoelectronics Progress
  • Vol. 60, Issue 16, 1610002 (2023)
Yan Hong1, Rong Pang1,*, Qing Wei2, Jingming Su1, and Feng Zhao1
Author Affiliations
  • 1School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, Anhui, China
  • 2School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221008, Jiangsu, China
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    DOI: 10.3788/LOP222380 Cite this Article Set citation alerts
    Yan Hong, Rong Pang, Qing Wei, Jingming Su, Feng Zhao. Nonlinear Adaptive Enhancement Algorithm for Uneven Illumination Images[J]. Laser & Optoelectronics Progress, 2023, 60(16): 1610002 Copy Citation Text show less
    Flow chart of proposed algorithm
    Fig. 1. Flow chart of proposed algorithm
    Images of illumination component. (a) (d) Original images; (b) (e) OTSU segmented images; (c) (f) images after mean segmentation
    Fig. 2. Images of illumination component. (a) (d) Original images; (b) (e) OTSU segmented images; (c) (f) images after mean segmentation
    Illumination component image histogram. (a) Image 1; (b) image 2
    Fig. 3. Illumination component image histogram. (a) Image 1; (b) image 2
    One-dimensional brightness information curves of L*(x,y) with different values of a. (a) Image 1; (b) image 2; (c) image 3; (d) image 4
    Fig. 4. One-dimensional brightness information curves of L*(x,y) with different values of a. (a) Image 1; (b) image 2; (c) image 3; (d) image 4
    Comparison of various algorithms for scab. (a) Original images; (b) MSRCR algorithm; (c) algorithm of literature [22]; (d) CLAHE algorithm; (e) proposed algorithm
    Fig. 5. Comparison of various algorithms for scab. (a) Original images; (b) MSRCR algorithm; (c) algorithm of literature [22]; (d) CLAHE algorithm; (e) proposed algorithm
    Comparison of various algorithms for juniper glue rust. (a) Original images; (b) MSRCR algorithm; (c) algorithm of literature [22]; (d) CLAHE algorithm; (e) proposed algorithm
    Fig. 6. Comparison of various algorithms for juniper glue rust. (a) Original images; (b) MSRCR algorithm; (c) algorithm of literature [22]; (d) CLAHE algorithm; (e) proposed algorithm
    Comparison of various algorithms for brown rot. (a) Original images; (b) MSRCR algorithm; (c) algorithm of literature [22]; (d) CLAHE algorithm; (e) proposed algorithm
    Fig. 7. Comparison of various algorithms for brown rot. (a) Original images; (b) MSRCR algorithm; (c) algorithm of literature [22]; (d) CLAHE algorithm; (e) proposed algorithm
    Grayscale surface diagrams of scab images. (a) (b) (c) Original images; (d) (e) (f) improved images of proposed algorithm
    Fig. 8. Grayscale surface diagrams of scab images. (a) (b) (c) Original images; (d) (e) (f) improved images of proposed algorithm
    Grayscale surface diagrams of juniper glue rust images. (a) (b) (c) Original images; (d) (e) (f) improved images of proposed algorithm
    Fig. 9. Grayscale surface diagrams of juniper glue rust images. (a) (b) (c) Original images; (d) (e) (f) improved images of proposed algorithm
    Grayscale surface diagrams of brown rot images. (a) (b) (c) Original images; (d) (e) (f) improved images of proposed algorithm
    Fig. 10. Grayscale surface diagrams of brown rot images. (a) (b) (c) Original images; (d) (e) (f) improved images of proposed algorithm
    Average changes of evaluation indexes of different algorithms
    Fig. 11. Average changes of evaluation indexes of different algorithms
    ImageParameter itemOriginalMSRCRAlgorithm of literature[22]CLAHEProposed algorithm
    Image 1Mean gradient0.41890.40790.40730.60080.6415
    Information entropy7.20386.86046.52407.46767.5787
    SSIM0.72650.73450.71520.7564
    PSNR11.163610.788515.807716.3187
    Image 2Mean gradient0.34750.46440.42670.65440.6590
    Information entropy6.65906.55896.29667.15067.2551
    SSIM0.77790.84920.62240.6871
    PSNR10.405211.414216.811316.8656
    Image 3Mean gradient0.27860.49120.41270.56480.5713
    Information entropy6.34867.30156.73577.10117.5818
    SSIM0.79600.87910.68020.8535
    PSNR9.276113.094516.175716.1757
    Table 1. Objective evaluation of scab images
    ImageParameter itemOriginalMSRCRAlgorithm of literature[22]CLAHEProposed algorithm
    Image 1Mean gradient0.28140.31500.42280.61580.6601
    Information entropy6.53006.75716.75687.22077.2627
    SSIM0.62230.86940.66920.7313
    PSNR11.211913.341016.632217.8120
    Image 2Mean gradient0.21370.42340.40400.54050.6872
    Information entropy6.17227.23276.83397.00387.2132
    SSIM0.76250.78360.66770.7675
    PSNR10.472012.808516.485316.0857
    Image 3Mean gradient0.25450.28610.28510.49220.5422
    Information entropy6.65926.69146.19627.24867.8148
    SSIM0.92400.79000.65760.6543
    PSNR8.714310.741816.901317.3359
    Table 2. Objective evaluation of juniper glue rust images
    ImageParameter itemOriginalMSRCRAlgorithm of literature[22]CLAHEProposed algorithm
    Image 1Mean gradient0.47880.36670.37110.73320.7520
    Information entropy7.38696.80256.23587.41827.4233
    SSIM0.85510.66310.61320.7681
    PSNR9.615910.647115.577116.0985
    Image 2Mean gradient0.42150.50960.49640.64660.6548
    Information entropy6.66227.08586.78697.44417.0513
    SSIM0.87120.89110.68400.7476
    PSNR8.938212.112216.055117.0568
    Image 3Mean gradient0.45430.51740.48250.63060.6812
    Information entropy6.55706.79606.32387.14347.4420
    SSIM0.94590.86340.68800.7740
    PSNR11.302213.371714.908915.1943
    Table 3. Objective evaluation of brown rot images
    Yan Hong, Rong Pang, Qing Wei, Jingming Su, Feng Zhao. Nonlinear Adaptive Enhancement Algorithm for Uneven Illumination Images[J]. Laser & Optoelectronics Progress, 2023, 60(16): 1610002
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