حذف نویز تصاویر حاصل از منابع تصویری آموزش ریاضی با استفاده از الگوریتم¬های مبتنی بر هوش مصنوعی
مهرداد نباهت
1
(
گروه ریاضی، واحد ارومیه، دانشگاه آزاد اسلامی، ارومیه، ایران
)
کلید واژه: فیلتر دوطرفه, الگوریتم بهینه¬سازی مبتنی بر کسب و انتشار دانش, فیلتر کوشی تعمیم یافته, الگوریتم بهینه¬سازی تراکم ذرات, الگوریتم بهینه¬سازی نهنگ.,
چکیده مقاله :
بهبود کیفیت تصاویر آموزشی ریاضی یک موضوع مهم در یادگیری دیجیتال است. بسیاری از تصاویر دریافتی از کتابها یا منابع آنلاین وضوح کافی ندارند و خطوط و نمادهای ریاضی در آنها بهخوبی دیده نمیشوند. روشهای معمول بهبود تصویر، کیفیت تصویر را بالا میبرند اما گاهی باعث محو شدن جزئیات مهم میشوند. برای حل این مشکل، در این کار از دو فیلتر هوشمند و الگوریتمهای هوش مصنوعی استفاده شده است. این روشها بهگونهای طراحی شدهاند که هم نویز و اعوجاج تصاویر کاهش یابد و هم وضوح خطوط، نمودارها و فرمولها حفظ شود. همچنین اندازه و پارامترهای فیلترها بهطور خودکار بهینهسازی میشوند تا بهترین نتیجه به دست آید. الگوریتمهای هوش مصنوعی استفاده شده عبارتند از الگوریتم بهینهسازی نهنگ و الگوریتم مبتنی بر کسب و انتشار دانش. معیارهای استفاده شده برای ارزیابی کیفیت تصویر نویز زدایی شده عبارت است از: PSNR، SSIM، FOM، EPF. نتایج نشان میدهد که این روشها در مقایسه با روشهای کلاسیک، تصاویر آموزشی واضحتر و دقیقتری تولید میکنند و درک مطالب ریاضی را برای دانشآموزان و دانشجویان آسانتر میسازند.
چکیده انگلیسی :
Enhancing the quality of mathematical educational images is a critical aspect of digital learning. Numerous images sourced from textbooks or online materials often lack sufficient clarity, rendering mathematical symbols and notations difficult to discern. Conventional image enhancement techniques can improve overall quality; however, they frequently result in the blurring of essential details. To address this limitation, the present study employs two intelligent filters in conjunction with artificial intelligence algorithms. These methods are designed to simultaneously reduce noise and distortions while preserving the sharpness of lines, graphs, and formulas. Furthermore, the filter parameters and configurations are automatically optimized to achieve optimal results. The artificial intelligence algorithms used include the whale optimization algorithm and the knowledge acquisition and dissemination algorithm. The criteria used to evaluate the quality of the denoised image are: PSNR, SSIM, FOM, EPF. Experimental findings demonstrate that, in comparison with traditional approaches, the proposed methodology produces clearer and more precise educational images, thereby facilitating a more effective comprehension of mathematical content for students.
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