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  <doi_batch_id>aspg-33-2144-1792222645</doi_batch_id>
  <timestamp>20261017073725</timestamp>
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  <journal>
   <journal_metadata language="en">
    <full_title>Galoitica: Journal of Mathematical Structures and Applications</full_title>
    <abbrev_title>GJMSA</abbrev_title>
    <issn media_type="electronic">2834-5568</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2023</year>
    </publication_date>
    <journal_volume>
     <volume>8</volume>
    </journal_volume>
    <issue>2</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Deep Learning for Super Resolution and Applications</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Zahraa</given_name>
      <surname>Hasan</surname>
      <affiliations>
       <institution>
        <institution_name>University Of Applied Science, Faculty of Literature and Science, Manama, Bahrain</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>High-resolution technologies are aimed at obtaining a high-resolution image from a low-resolution image, and the importance of this field has increased due to the emergence of the need to have high-resolution images in many important applications such as medical, security, and other images. Methods for obtaining ultra-high-resolution images have developed after the advent of Deep Learning Technologies, which have shown good results in this task, Due to the importance of the field of ultra-high-resolution images and deep learning, In this article we will explain one of the deep learning models used to obtain a high-resolution image from a low-resolution image and how to build and train it based on one of the famous deep learning offices and using one of the google platforms used in training, namely Google Laboratory</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2023</year>
    </publication_date>
    <pages>
     <first_page>34</first_page>
     <last_page>42</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">2144</item_number>
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     <doi>10.54216/GJMSA.080204</doi>
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