Improving Super-Resolution in Face Images by Modeling Image Degradation Using Pairs of High-Quality and Low-Quality Images
<p><span style="font-size: 10.0pt; line-height: 125%; font-family: 'Georgia',serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Georgia; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman';...
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| Format: | Article |
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Islamic Azad University Bushehr Branch
2025-01-01
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| Series: | مهندسی مخابرات جنوب |
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| Online Access: | https://sanad.iau.ir/journal/jce/Article/1092455 |
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| author | Ahmad Dolatkhah |
| author_facet | Ahmad Dolatkhah |
| author_sort | Ahmad Dolatkhah |
| collection | DOAJ |
| description | <p><span style="font-size: 10.0pt; line-height: 125%; font-family: 'Georgia',serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Georgia; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: JA; mso-bidi-language: AR-SA;"> </span><span style="font-size: 11pt; line-height: 125%; font-family: Calibri, sans-serif;">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</span></p> |
| format | Article |
| id | doaj-art-40f0e235f7f54480a8b0f9bbb46420a3 |
| institution | Kabale University |
| issn | 2980-9231 |
| language | fas |
| publishDate | 2025-01-01 |
| publisher | Islamic Azad University Bushehr Branch |
| record_format | Article |
| series | مهندسی مخابرات جنوب |
| spelling | doaj-art-40f0e235f7f54480a8b0f9bbb46420a32025-01-11T05:06:07ZfasIslamic Azad University Bushehr Branchمهندسی مخابرات جنوب2980-92312025-01-0114546982Improving Super-Resolution in Face Images by Modeling Image Degradation Using Pairs of High-Quality and Low-Quality ImagesAhmad Dolatkhah0Department of information and Communication, Amin Police University, Tehran, Iran <p><span style="font-size: 10.0pt; line-height: 125%; font-family: 'Georgia',serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Georgia; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: JA; mso-bidi-language: AR-SA;"> </span><span style="font-size: 11pt; line-height: 125%; font-family: Calibri, sans-serif;">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</span></p>https://sanad.iau.ir/journal/jce/Article/1092455quality of face image adversarial generative network super-resolution deep learning. |
| spellingShingle | Ahmad Dolatkhah Improving Super-Resolution in Face Images by Modeling Image Degradation Using Pairs of High-Quality and Low-Quality Images مهندسی مخابرات جنوب quality of face image adversarial generative network super-resolution deep learning. |
| title | Improving Super-Resolution in Face Images by Modeling Image Degradation Using Pairs of High-Quality and Low-Quality Images |
| title_full | Improving Super-Resolution in Face Images by Modeling Image Degradation Using Pairs of High-Quality and Low-Quality Images |
| title_fullStr | Improving Super-Resolution in Face Images by Modeling Image Degradation Using Pairs of High-Quality and Low-Quality Images |
| title_full_unstemmed | Improving Super-Resolution in Face Images by Modeling Image Degradation Using Pairs of High-Quality and Low-Quality Images |
| title_short | Improving Super-Resolution in Face Images by Modeling Image Degradation Using Pairs of High-Quality and Low-Quality Images |
| title_sort | improving super resolution in face images by modeling image degradation using pairs of high quality and low quality images |
| topic | quality of face image adversarial generative network super-resolution deep learning. |
| url | https://sanad.iau.ir/journal/jce/Article/1092455 |
| work_keys_str_mv | AT ahmaddolatkhah improvingsuperresolutioninfaceimagesbymodelingimagedegradationusingpairsofhighqualityandlowqualityimages |