Review Article


DOI :10.26650/acin.765320   IUP :10.26650/acin.765320    Full Text (PDF)

A Survey on Image Super-Resolution with Generative Adversarial Networks

Hürkal HüsemZeynep Orman

Super-resolution is a process to increase image dimensions with a specific upscaling factor while trying to preserve details that matche with the original high-resolution form. Super-resolution can be done with many techniques. But the most effective technique is the one that takes advantage of several neural network designs. Some network designs are more appropriate than others on the specific subject. This study focuses on super resolution studies using Generative Adversarial Network. Many studies use this neural network type to look at various topics such as artificial data production and making the data more meaningful. The key point of this neural network type is having two different sub-networks that try to defeat each other in order to make more realistic results. Performance metrics that measure the quality of a generated image, loss functions used in a neural network and research papers on super-resolution with Generative Adversarial Network are the main domains of this study. 

DOI :10.26650/acin.765320   IUP :10.26650/acin.765320    Full Text (PDF)

Üretken Çekişmeli Ağlar ile Görsel Çözünürlük Artırımı Üzerine Bir Araştırma

Hürkal HüsemZeynep Orman

Super-resolution is a process to increase image dimensions with a specific upscaling factor while trying to preserve details that matche with the original high-resolution form. Super-resolution can be done with many techniques. But the most effective technique is the one that takes advantage of several neural network designs. Some network designs are more appropriate than others on the specific subject. This study focuses on super resolution studies using Generative Adversarial Network. Many studies use this neural network type to look at various topics such as artificial data production and making the data more meaningful. The key point of this neural network type is having two different sub-networks that try to defeat each other in order to make more realistic results. Performance metrics that measure the quality of a generated image, loss functions used in a neural network and research papers on super-resolution with Generative Adversarial Network are the main domains of this study. 


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APA

Hüsem, H., & Orman, Z. (2020). A Survey on Image Super-Resolution with Generative Adversarial Networks. Acta Infologica, 4(2), 139-154. https://doi.org/10.26650/acin.765320


AMA

Hüsem H, Orman Z. A Survey on Image Super-Resolution with Generative Adversarial Networks. Acta Infologica. 2020;4(2):139-154. https://doi.org/10.26650/acin.765320


ABNT

Hüsem, H.; Orman, Z. A Survey on Image Super-Resolution with Generative Adversarial Networks. Acta Infologica, [Publisher Location], v. 4, n. 2, p. 139-154, 2020.


Chicago: Author-Date Style

Hüsem, Hürkal, and Zeynep Orman. 2020. “A Survey on Image Super-Resolution with Generative Adversarial Networks.” Acta Infologica 4, no. 2: 139-154. https://doi.org/10.26650/acin.765320


Chicago: Humanities Style

Hüsem, Hürkal, and Zeynep Orman. A Survey on Image Super-Resolution with Generative Adversarial Networks.” Acta Infologica 4, no. 2 (May. 2021): 139-154. https://doi.org/10.26650/acin.765320


Harvard: Australian Style

Hüsem, H & Orman, Z 2020, 'A Survey on Image Super-Resolution with Generative Adversarial Networks', Acta Infologica, vol. 4, no. 2, pp. 139-154, viewed 9 May. 2021, https://doi.org/10.26650/acin.765320


Harvard: Author-Date Style

Hüsem, H. and Orman, Z. (2020) ‘A Survey on Image Super-Resolution with Generative Adversarial Networks’, Acta Infologica, 4(2), pp. 139-154. https://doi.org/10.26650/acin.765320 (9 May. 2021).


MLA

Hüsem, Hürkal, and Zeynep Orman. A Survey on Image Super-Resolution with Generative Adversarial Networks.” Acta Infologica, vol. 4, no. 2, 2020, pp. 139-154. [Database Container], https://doi.org/10.26650/acin.765320


Vancouver

Hüsem H, Orman Z. A Survey on Image Super-Resolution with Generative Adversarial Networks. Acta Infologica [Internet]. 9 May. 2021 [cited 9 May. 2021];4(2):139-154. Available from: https://doi.org/10.26650/acin.765320 doi: 10.26650/acin.765320


ISNAD

Hüsem, Hürkal - Orman, Zeynep. A Survey on Image Super-Resolution with Generative Adversarial Networks”. Acta Infologica 4/2 (May. 2021): 139-154. https://doi.org/10.26650/acin.765320



TIMELINE


Submitted07.07.2020
Accepted17.08.2020
Published Online31.12.2020

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