بهبود فراتفکیک پذیری در تصاویر چهره بوسیله مدلسازی خرابی تصویر با استفاده از زوج تصاویر با کیفیت و بی‌کیفیت
محورهای موضوعی : مهندسی الکترونیک
1 - گروه فناوری اطلاعات و ارتباطات دانشگاه علوم انتظامی امین، تهران
کلید واژه: افزایش کیفیت تصویر چهره, شبکه مولد تخاصمی, فراتفکیک پذیری, یادگیری عمیق,
چکیده مقاله :
بهبود کیفیت تصویر جهت شناسایی و احراز هویت در سیستم های امنیتی و نظارتی دارای اهمیت ویژه بوده و امروزه با استفاده از هوش مصنوعی می توان کیفیت تصاویر را به صورت قابل توجهی بهبود داد. در این راستا مقاله حاضر با تمرکز بر جزئیات تصاویر چهره، مدل تشخیص خرابی تصویر در شبکه مولد تخاصمی را بهبود داده است که منجر به عملکرد مناسب در فراتفکیک پذیری تصاویر چهره شد. اکثر شبکه‌های CNN که در سالهای اخیر ارائه شده است، برای عملکرد مناسب نیاز به مجموعه تصاویر بسیار زیاد با حاشیه نویسی مناسب دارند و معمولا در مورد خرابی‌هایی که آموزش ندیده‌اند عملکرد نامناسبی دارند که در این مقاله به بهبود این چالش پرداخته شده است. در این کار برای آموزش مدل تشخیص خرابی تصویر، از جفت تصویرهای با کیفیت و بی‌کیفت استفاده شده است؛ سپس این اطلاعات به تصاویر واقعی انتقال داده می‌شوند. طبیعی بودن تصاویر خروجی از مهمترین چالش‌های موجود در این زمینه است. نتایج بدست آمده نشان می دهد که معیار شباهت ادراکی تصویر به دست آمده برابر با 38.4% بوده که نسبت به پژوهش‌های اخیر قابل مقایسه می باشد. در نتیجه با استفاده از مدل پیشنهادی، تصاویر طبیعی‌تری تولید شد.
Improving image quality for identification and authentication in security and surveillance systems is of particular importance, and today, using artificial intelligence, the quality of images can be significantly improved. In this regard, the present paper, focusing on the details of face images, has improved the image failure detection model in the adversarial generator network, which led to a suitable performance in the meta-dissolving of face images. Most of the CNN networks that have been presented in recent years require a large set of images with appropriate annotations for proper performance, and they usually perform poorly in the case of degradation that have not been trained, which is addressed in this research to improve this challenge. In this work, pairs of high-quality and low-quality images are used to train the image degradation detection model; This information is then transferred to real images. The naturalness of the output images is one of the most important challenges in this field. The obtained results show that the criterion of perceptual similarity of the obtained image is equal to 38.4%, which is comparable to recent researches. As a result, using the proposed model, more natural images were produced
بهبود وضوح فوق العاده در تصاویر چهره با مدل سازی تخریب تصویر با استفاده از تصاویر با کیفیت بالا و کم کیفیت.
بهبود کیفیت تصویر برای شناسایی و احراز هویت در سیستم های امنیتی و نظارتی.
با استفاده از شبکه های SynNet و DegNet، مدل تشخیص آسیب تصویر بهبود یافت و جزئیات تصویر حفظ شد.
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