Image Visual Quality: Sharpness Evaluation in the Logarithmic Image Processing Framework
In image processing, the acquisition step plays a fundamental role because it determines image quality. The present paper focuses on the issue of blur and suggests ways of assessing contrast. The logic of this work consists in evaluating the sharpness of an image by means of objective measures based...
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2025-06-01
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| author | Arnaud Pauwelyn Maxime Carré Michel Jourlin Dominique Ginhac Fabrice Meriaudeau |
| author_facet | Arnaud Pauwelyn Maxime Carré Michel Jourlin Dominique Ginhac Fabrice Meriaudeau |
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| description | In image processing, the acquisition step plays a fundamental role because it determines image quality. The present paper focuses on the issue of blur and suggests ways of assessing contrast. The logic of this work consists in evaluating the sharpness of an image by means of objective measures based on mathematical, physical, and optical justifications in connection with the human visual system. This is why the Logarithmic Image Processing (LIP) framework was chosen. The sharpness of an image is usually assessed near objects’ boundaries, which encourages the use of gradients, with some major drawbacks. Within the LIP framework, it is possible to overcome such problems using a “contour detector” tool based on the notion of Logarithmic Additive Contrast (LAC). Considering a sequence of images increasingly blurred, we show that the use of LAC enables images to be re-classified in accordance with their defocus level, demonstrating the relevance of the method. The proposed algorithm has been shown to outperform five conventional methods for assessing image sharpness. Moreover, it is the only method that is insensitive to brightness variations. Finally, various application examples are presented, like automatic autofocus control or the comparison of two blur removal algorithms applied to the same image, which particularly concerns the field of Super Resolution (SR) algorithms. Such algorithms multiply (×2, ×3, ×4) the resolution of an image using powerful tools (deep learning, neural networks) while correcting the potential defects (blur, noise) that could be generated by the resolution extension itself. We conclude with the prospects for this work, which should be part of a broader approach to estimating image quality, including sharpness and perceived contrast. |
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| language | English |
| publishDate | 2025-06-01 |
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| series | Big Data and Cognitive Computing |
| spelling | doaj-art-07ae09ef1c9a4f6fb7daa368ef0aa0222025-08-20T02:24:26ZengMDPI AGBig Data and Cognitive Computing2504-22892025-06-019615410.3390/bdcc9060154Image Visual Quality: Sharpness Evaluation in the Logarithmic Image Processing FrameworkArnaud Pauwelyn0Maxime Carré1Michel Jourlin2Dominique Ginhac3Fabrice Meriaudeau4NT2I (Nouvelles Technologies Ingénierie Innovation), 18, Rue Jean Servanton, 42000 Saint-Etienne, FranceNT2I (Nouvelles Technologies Ingénierie Innovation), 18, Rue Jean Servanton, 42000 Saint-Etienne, FranceLaboratoire Hubert Curien, CNRS, Université Jean Monnet, 18, Rue Professeur Benoît Lauras, 42000 Saint-Etienne, FranceICB (Laboratoire Interdisciplinaire Carnot de Bourgogne), CNRS, Université Bourgogne Europe, 21078 Dijon, FranceIFTIM (Imagerie Fonctionnelle et moléculaire et Traitement des Images Médicales), ICMUB (Institut de Chimie Moléculaire), CNRS, Université de Bourgogne, 9, Avenue Alain Savary, 21078 Dijon, FranceIn image processing, the acquisition step plays a fundamental role because it determines image quality. The present paper focuses on the issue of blur and suggests ways of assessing contrast. The logic of this work consists in evaluating the sharpness of an image by means of objective measures based on mathematical, physical, and optical justifications in connection with the human visual system. This is why the Logarithmic Image Processing (LIP) framework was chosen. The sharpness of an image is usually assessed near objects’ boundaries, which encourages the use of gradients, with some major drawbacks. Within the LIP framework, it is possible to overcome such problems using a “contour detector” tool based on the notion of Logarithmic Additive Contrast (LAC). Considering a sequence of images increasingly blurred, we show that the use of LAC enables images to be re-classified in accordance with their defocus level, demonstrating the relevance of the method. The proposed algorithm has been shown to outperform five conventional methods for assessing image sharpness. Moreover, it is the only method that is insensitive to brightness variations. Finally, various application examples are presented, like automatic autofocus control or the comparison of two blur removal algorithms applied to the same image, which particularly concerns the field of Super Resolution (SR) algorithms. Such algorithms multiply (×2, ×3, ×4) the resolution of an image using powerful tools (deep learning, neural networks) while correcting the potential defects (blur, noise) that could be generated by the resolution extension itself. We conclude with the prospects for this work, which should be part of a broader approach to estimating image quality, including sharpness and perceived contrast.https://www.mdpi.com/2504-2289/9/6/154image qualityblursharpnesscontrastlogarithmic image processingautofocus |
| spellingShingle | Arnaud Pauwelyn Maxime Carré Michel Jourlin Dominique Ginhac Fabrice Meriaudeau Image Visual Quality: Sharpness Evaluation in the Logarithmic Image Processing Framework Big Data and Cognitive Computing image quality blur sharpness contrast logarithmic image processing autofocus |
| title | Image Visual Quality: Sharpness Evaluation in the Logarithmic Image Processing Framework |
| title_full | Image Visual Quality: Sharpness Evaluation in the Logarithmic Image Processing Framework |
| title_fullStr | Image Visual Quality: Sharpness Evaluation in the Logarithmic Image Processing Framework |
| title_full_unstemmed | Image Visual Quality: Sharpness Evaluation in the Logarithmic Image Processing Framework |
| title_short | Image Visual Quality: Sharpness Evaluation in the Logarithmic Image Processing Framework |
| title_sort | image visual quality sharpness evaluation in the logarithmic image processing framework |
| topic | image quality blur sharpness contrast logarithmic image processing autofocus |
| url | https://www.mdpi.com/2504-2289/9/6/154 |
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