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Non-linear weight adjustment in adaptive gamma correction for image contrast enhancement

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A Correction to this article was published on 08 November 2020

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Abstract

Image enhancement remains an intricate problem, crucial for image analysis. Several algorithms exist for the same. A few among these algorithms categorize images into different classes based on their statistical parameters and apply separate enhancement functions for each class. One such algorithm is the well-known adaptive gamma correction (AGC) algorithm. It works well for each class of images, but fails when the statistical parameters lie on the boundary of separation of two classes. We have developed an enhancement algorithm which can enhance images which lie on the boundary of separation equally well, as images which lie deep inside the boundary. The basic idea behind the algorithm is to combine the different enhancement functions of AGC using non-linear weight adjustments. Both contrast and brightness have been modified using these weight adjustments. We have conducted experiments on a data-set consisting of 9979 images. Results show that by using the proposed algorithm, average entropy of the enhanced images increases by 3.97% and average root mean square (rms) increases by 14.29% over AGC. Visual improvement is also perceivable.

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  • 08 November 2020

    Figure 16 in the original publication was incomplete. The original article has been corrected.

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Acknowledgements

We are thankful to the reviewers for their valuable comments which have helped to improve the quality of the paper. We also thank Md. Sahidullah and Shefali Waldekar, for their critical comments and for correction of English grammar and punctuation.

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Authors and Affiliations

  1. Indian Institute of Engineering Science and Technology, Howrah, India

    Debapriya Sengupta & Arindam Biswas

  2. National Institute of Technical Teachers’ Training and Research, Kolkata, India

    Debapriya Sengupta

  3. GLA University, Mathura, India

    Phalguni Gupta

Authors
  1. Debapriya Sengupta
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  2. Arindam Biswas
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  3. Phalguni Gupta
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Correspondence to Debapriya Sengupta.

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The original online version of this article was revised: Figure 16 was incomplete.

Appendix

Appendix

A 3D view of the variations of γ and γν, as μ varies from 0 to 1 and σ varies from 0 to 0.5 is shown in Fig. 17. Similarly, a 3D view showing the variations of c and cν, as μ varies from 0 to 1 and γ or γν varies from 0.779 to 50, for intensity 0.996, is shown in Fig. 18. The difference between the continuous and discontinuous nature of the plots is evident from these figures.

Fig. 17
figure 17

3D view of variation of γ and γν as μ and σ varies

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Fig. 18
figure 18

3D view of variation of c and cν as μ, γ or γν varies. Intensity = 0.996

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Sengupta, D., Biswas, A. & Gupta, P. Non-linear weight adjustment in adaptive gamma correction for image contrast enhancement. Multimed Tools Appl 80, 3835–3862 (2021). https://doi.org/10.1007/s11042-020-09583-1

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