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Enhancing colour image contrast via analytic functions subordinate to generalized Mersenne polynomials.

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Title: Enhancing colour image contrast via analytic functions subordinate to generalized Mersenne polynomials.
Authors: Aarthy, B.1 (AUTHOR), Keerthi, B. Srutha1 (AUTHOR) keerthivitmaths@gmail.com
Source: Imaging Science Journal. Oct2025, Vol. 73 Issue 7, p815-830. 16p.
Subjects: Contrast effect, Image enhancement (Imaging systems), Image quality analysis, Polynomials, Analytic functions
Abstract: Digitally captured and transferred colour images often suffer from low contrast, impacting both human perception and automated system performance. To address this issue with minimal information loss, we propose a new contrast enhancement method using a transformation function derived from Sakaguchi type functions subordinate to Generalized Mersenne polynomials on the open unit disk. This pixel-wise transformation enhances image intensity and contrast effectively. The method is particularly well-suited for colour images, producing high-quality outputs while preserving image details. Its simplicity allows application across various image types with varying contrast degradation. We evaluate our approach on 275 low-contrast images from two datasets - Categorical Image Quality (CSIQ) Database and Tampere Image Database (TID2013) - across five distortion levels. Also, a comparative study with other state-of-the-art techniques demonstrates that the proposed method achieves superior visual quality. [ABSTRACT FROM AUTHOR]
Copyright of Imaging Science Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Enhancing colour image contrast via analytic functions subordinate to generalized Mersenne polynomials.
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  Data: <searchLink fieldCode="JN" term="%22Imaging+Science+Journal%22">Imaging Science Journal</searchLink>. Oct2025, Vol. 73 Issue 7, p815-830. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Contrast+effect%22">Contrast effect</searchLink><br /><searchLink fieldCode="DE" term="%22Image+enhancement+%28Imaging+systems%29%22">Image enhancement (Imaging systems)</searchLink><br /><searchLink fieldCode="DE" term="%22Image+quality+analysis%22">Image quality analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Polynomials%22">Polynomials</searchLink><br /><searchLink fieldCode="DE" term="%22Analytic+functions%22">Analytic functions</searchLink>
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  Data: Digitally captured and transferred colour images often suffer from low contrast, impacting both human perception and automated system performance. To address this issue with minimal information loss, we propose a new contrast enhancement method using a transformation function derived from Sakaguchi type functions subordinate to Generalized Mersenne polynomials on the open unit disk. This pixel-wise transformation enhances image intensity and contrast effectively. The method is particularly well-suited for colour images, producing high-quality outputs while preserving image details. Its simplicity allows application across various image types with varying contrast degradation. We evaluate our approach on 275 low-contrast images from two datasets - Categorical Image Quality (CSIQ) Database and Tampere Image Database (TID2013) - across five distortion levels. Also, a comparative study with other state-of-the-art techniques demonstrates that the proposed method achieves superior visual quality. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Imaging Science Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/13682199.2025.2495497
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      – Code: eng
        Text: English
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        PageCount: 16
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        Type: general
      – SubjectFull: Image enhancement (Imaging systems)
        Type: general
      – SubjectFull: Image quality analysis
        Type: general
      – SubjectFull: Polynomials
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      – SubjectFull: Analytic functions
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              Text: Oct2025
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