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. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 13682199 |
| DOI: | 10.1080/13682199.2025.2495497 |
