Enhancing the quality of low-light images via the coefficient bounds derived for a subclass of Sakaguchi-type function
Abstract This paper introduces a new approach for enhancing low-light images by utilizing coefficient bounds from a specific subclass of analytic functions, a concept rooted in geometric function theory. Our method is designed to adapt dynamically to different lighting conditions, ensuring effective...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-02-01
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| Series: | EURASIP Journal on Image and Video Processing |
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| Online Access: | https://doi.org/10.1186/s13640-025-00663-6 |
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| author | K. Sivagami Sundari B. Srutha Keerthi |
| author_facet | K. Sivagami Sundari B. Srutha Keerthi |
| author_sort | K. Sivagami Sundari |
| collection | DOAJ |
| description | Abstract This paper introduces a new approach for enhancing low-light images by utilizing coefficient bounds from a specific subclass of analytic functions, a concept rooted in geometric function theory. Our method is designed to adapt dynamically to different lighting conditions, ensuring effective image enhancement in both uniformly and non-uniformly illuminated environments. Specifically, we apply a convolution process for images that are evenly illuminated, while a power-law transformation is employed when illumination is non-uniform. This dual-method strategy allows us to overcome common challenges such as over-enhancement and under-enhancement, which are frequently encountered in traditional approaches. By adjusting to varying lighting conditions, our approach guarantees superior image quality across a range of scenarios. Through extensive experiments and rigorous comparisons using performance metrics, we demonstrate that our method consistently outperforms existing techniques, yielding significant improvements in both image clarity and contrast. These results confirm the effectiveness and adaptability of our approach in enhancing low-light images. |
| format | Article |
| id | doaj-art-537a540a476b49278136c1c02c48fc29 |
| institution | Kabale University |
| issn | 1687-5281 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Image and Video Processing |
| spelling | doaj-art-537a540a476b49278136c1c02c48fc292025-02-09T12:50:12ZengSpringerOpenEURASIP Journal on Image and Video Processing1687-52812025-02-012025111610.1186/s13640-025-00663-6Enhancing the quality of low-light images via the coefficient bounds derived for a subclass of Sakaguchi-type functionK. Sivagami Sundari0B. Srutha Keerthi1Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology Chennai CampusDepartment of Mathematics, School of Advanced Sciences, Vellore Institute of Technology Chennai CampusAbstract This paper introduces a new approach for enhancing low-light images by utilizing coefficient bounds from a specific subclass of analytic functions, a concept rooted in geometric function theory. Our method is designed to adapt dynamically to different lighting conditions, ensuring effective image enhancement in both uniformly and non-uniformly illuminated environments. Specifically, we apply a convolution process for images that are evenly illuminated, while a power-law transformation is employed when illumination is non-uniform. This dual-method strategy allows us to overcome common challenges such as over-enhancement and under-enhancement, which are frequently encountered in traditional approaches. By adjusting to varying lighting conditions, our approach guarantees superior image quality across a range of scenarios. Through extensive experiments and rigorous comparisons using performance metrics, we demonstrate that our method consistently outperforms existing techniques, yielding significant improvements in both image clarity and contrast. These results confirm the effectiveness and adaptability of our approach in enhancing low-light images.https://doi.org/10.1186/s13640-025-00663-6Analytic functionsLow-light imagesConvolutionPower-law transformationEuler polynomial |
| spellingShingle | K. Sivagami Sundari B. Srutha Keerthi Enhancing the quality of low-light images via the coefficient bounds derived for a subclass of Sakaguchi-type function EURASIP Journal on Image and Video Processing Analytic functions Low-light images Convolution Power-law transformation Euler polynomial |
| title | Enhancing the quality of low-light images via the coefficient bounds derived for a subclass of Sakaguchi-type function |
| title_full | Enhancing the quality of low-light images via the coefficient bounds derived for a subclass of Sakaguchi-type function |
| title_fullStr | Enhancing the quality of low-light images via the coefficient bounds derived for a subclass of Sakaguchi-type function |
| title_full_unstemmed | Enhancing the quality of low-light images via the coefficient bounds derived for a subclass of Sakaguchi-type function |
| title_short | Enhancing the quality of low-light images via the coefficient bounds derived for a subclass of Sakaguchi-type function |
| title_sort | enhancing the quality of low light images via the coefficient bounds derived for a subclass of sakaguchi type function |
| topic | Analytic functions Low-light images Convolution Power-law transformation Euler polynomial |
| url | https://doi.org/10.1186/s13640-025-00663-6 |
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