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Adaptive fuzzy intensity measure enhancement technique for non-uniform illumination and low-contrast images

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Abstract

A new enhancement technique based on fuzzy intensity measure is proposed in this study to address problems in non-uniform illumination and low contrast often encountered in recorded images. The proposed algorithm, namely adaptive fuzzy intensity measure, is capable of selectively enhancing dark region without increasing illumination in bright region. A fuzzy intensity measure is calculated to determine the intensity distribution of the original image and distinguish between bright and dark regions. Image illumination is improved, whereas local contrast of the image is increased to ensure detail preservation. Implementation of the proposed technique on grayscale and color images with non-uniform illumination images shows that in most cases (i.e., except for processing time), the proposed technique is superior compared with other state-of-the-art techniques. The proposed technique produces images with homogeneous illumination. In addition, the proposed method is computationally fast (i.e., \(<\)1 s) and thus can be utilized in real-time applications.

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Notes

  1. In the GC approach, the value of gamma is chosen based on the optimization procedure as presented in Sect. 4. However, for this approach, the gamma values are incremented from 0.1 to 1.0 and gamma value that produces the maximum \(Q\) is chosen as the optimum value of gamma.

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Author information

Authors and Affiliations

  1. Imaging and Intelligent System Research Team (ISRT), School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 , Nibong Tebal, Penang, Malaysia

    Khairunnisa Hasikin & Nor Ashidi Mat Isa

  2. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 , Lembah Pantai, Kuala Lumpur, Malaysia

    Khairunnisa Hasikin

Authors
  1. Khairunnisa Hasikin
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  2. Nor Ashidi Mat Isa
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Corresponding author

Correspondence to Nor Ashidi Mat Isa.

Additional information

This project is supported by the Ministry of Science, Technology & Innovation Malaysia through Sciencefund Grant entitled “Development of Computational Intelligent Infertility Detection System based on Sperm Motility Analysis”.

Appendices

Appendix 1

See Tables 3 and 4.

Table 3 Comparison of enhancement techniques for grayscale images
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Table 4 Comparison of enhancement techniques for color images
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Appendix 2

See Tables 5, 6, 7 and 8.

Table 5 Comparison of enhancement results based on image contrast \((C)\)
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Table 6 Comparison of enhancement results based on image quality index \((Q)\)
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Table 7 Comparison of enhancement results based on PL
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Table 8 Comparison of enhancement results based on processing time \((t)\) (in s)
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Appendix 3

1.1 Quantitative analysis for grayscale images

See Figs. 10, 11, 12, and 13.

Fig. 10
figure 10

Error bars of image contrast for different enhancement techniques. Graph is plotted by computing average and standard deviation of image contrast for 150 grayscale images

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Fig. 11
figure 11

Error bars of image quality index for different enhancement techniques. Graph is plotted by computing average and standard deviation of image contrast for 150 grayscale images

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Fig. 12
figure 12

Error bars of PL for different enhancement techniques. Graph is plotted by computing average and standard deviation of image contrast for 150 grayscale images

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Fig. 13
figure 13

Error bars of processing time for different enhancement techniques. Graph is plotted by computing average and standard deviation of image contrast for 150 grayscale image

Full size image

Appendix 4

1.1 Quantitative analysis for color images

See Figs. 14, 15, 16, and 17.

Fig. 14
figure 14

Error bars of image contrast for different enhancement techniques. Graph is plotted by computing average and standard deviation of image contrast for 150 color images

Full size image
Fig. 15
figure 15

Error bars of image quality index for different enhancement techniques. Graph is plotted by computing average and standard deviation of image contrast for 150 color images

Full size image
Fig. 16
figure 16

Error bars of PL for different enhancement techniques. Graph is plotted by computing average and standard deviation of image contrast for 150 color images

Full size image
Fig. 17
figure 17

Error bars of processing time for different enhancement techniques. Graph is plotted by computing average and standard deviation of image

Full size image

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Hasikin, K., Mat Isa, N.A. Adaptive fuzzy intensity measure enhancement technique for non-uniform illumination and low-contrast images. SIViP 9, 1419–1442 (2015). https://doi.org/10.1007/s11760-013-0596-1

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