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<p>Image sharpening is a fundamental technique in image processing used to enhance the clarity and detail of images by improving edge contrast. Traditional sharpening methods often introduce artifacts and noise, particularly in low-contrast regions. In this paper, we propose a new approach to image sharpening based on coefficient bounds obtained for a subclass of Sakaguchi-type analytic functions subordinate to the generating function of Gregory coefficients. We derive the initial coefficients and incorporate these bounds as adaptive sharpening factors in a modified unsharp masking framework. The proposed method effectively enhances edge definition while preserving the natural appearance of smoother regions. To validate its performance, we apply the technique to various benchmark datasets, including CSIQ, LIVE, TID2013, and KADID 10k, and evaluate sharpness improvement using Pearson linear correlation coefficient and Spearman’s rank ordered correlation coefficient. This study highlights the potential of geometric function theory in advancing image processing techniques and opens new avenues for interdisciplinary research in mathematical imaging.</p>

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Enhancing Image Sharpness by Modified Unsharp Masking Using Coefficient Bounds Obtained for a Subclass of Analytic Functions

  • 期刊论文

  • 发表日期：

    2025年8月1日

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  • B. Aarthy,
  • B. Srutha Keerthi

摘要

Image sharpening is a fundamental technique in image processing used to enhance the clarity and detail of images by improving edge contrast. Traditional sharpening methods often introduce artifacts and noise, particularly in low-contrast regions. In this paper, we propose a new approach to image sharpening based on coefficient bounds obtained for a subclass of Sakaguchi-type analytic functions subordinate to the generating function of Gregory coefficients. We derive the initial coefficients and incorporate these bounds as adaptive sharpening factors in a modified unsharp masking framework. The proposed method effectively enhances edge definition while preserving the natural appearance of smoother regions. To validate its performance, we apply the technique to various benchmark datasets, including CSIQ, LIVE, TID2013, and KADID 10k, and evaluate sharpness improvement using Pearson linear correlation coefficient and Spearman’s rank ordered correlation coefficient. This study highlights the potential of geometric function theory in advancing image processing techniques and opens new avenues for interdisciplinary research in mathematical imaging.