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AuthorElasri, Mohamed
AuthorElharrouss, Omar
AuthorAl-Maadeed, Somaya
AuthorTairi, Hamid
Available date2023-02-23T09:13:04Z
Publication Date2022
Publication NameNeural Processing Letters
ResourceScopus
URIhttp://dx.doi.org/10.1007/s11063-022-10777-x
URIhttp://hdl.handle.net/10576/40334
AbstractThe creation of an image from another and from different types of data including text, scene graph, and object layout, is one of the very challenging tasks in computer vision. In addition, capturing images from different views for generating an object or a product can be exhaustive and expansive to do manually. Now, using deep learning and artificial intelligence techniques, the generation of new images from different type of data has become possible. For that, a significant effort has been devoted recently to develop image generation strategies with a great achievement. To that end, we present in this paper, to the best of the authors' knowledge, the first comprehensive overview of existing image generation methods. Accordingly, a description of each image generation technique is performed based on the nature of the adopted algorithms, type of data used, and main objective. Moreover, each image generation category is discussed by presenting the proposed approaches. In addition, a presentation of existing image generation datasets is given. The evaluation metrics that are suitable for each image generation category are discussed and a comparison of the performance of existing solutions is provided to better inform the state-of-the-art and identify their limitations and strengths. Lastly, the current challenges that are facing this subject are presented.
Languageen
PublisherSpringer
SubjectImage generation
Image-to-image translation
Layout-to-image generation
Panoramic image generation
Sketch-to-image generation
Text-to-image generation
TitleImage Generation: A Review
TypeArticle Review
Pagination4609-4646
Issue Number5
Volume Number54
dc.accessType Abstract Only


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