Enhanced Super-Resolution Using GAN Article Swipe

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Chandan Kumar , Amzad Choudhary , Gurpreet Singh , Ms. Deepti Gupta ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.22214/ijraset.2022.42718

Super-resolution reconstruction is an increasingly important area in computer vision. To eliminate the problems that super-resolution reconstruction models based on generative adversarial networks are difficult to train and contain artifacts in reconstruction results. besides the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks. However, the hallucinated details are often accompanied with unpleasant artifacts. This paper presented ESRGAN model which was also based on generative adversarial networks. To further enhance the visual quality, we thoroughly study three key components of SRGAN – network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN). In particular, we introduce the Residual-in-Residual Dense Block (RRDB) without batch normalization as the basic network building unit. Moreover, we borrow the idea from relativistic GAN to let the discriminator predict relative realness instead of the absolute value. Finally, we improve the perceptual loss by using the features before activation, which could provide stronger supervision for brightness consistency and texture recovery. Benefiting from these improvements, the proposed ESRGAN achieves consistently better visual quality with more realistic and natural textures than SRGAN.

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Hallucination

Computer Science

Artificial Intelligence

Convolutional Neural Network

Concepts

Hallucinating Discriminator Computer science Residual Normalization (sociology) Artificial intelligence Block (permutation group theory) Convolutional neural network Adversarial system Generative grammar Computer vision Algorithm Mathematics Geometry Telecommunications Detector Anthropology Sociology

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Type: article
Language: en
Landing Page: http://doi.org/10.22214/ijraset.2022.42718
PDF: https://doi.org/10.22214/ijraset.2022.42718
OA Status: diamond
Cited By: 2
References: 10
Related Works: 10
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DOI: https://doi.org/10.22214/ijraset.2022.42718

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Title: Enhanced Super-Resolution Using GAN

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Type: article

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Language: en

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Publication year: 2022

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Publication date: 2022-05-27

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Authors: Chandan Kumar, Amzad Choudhary, Gurpreet Singh, Ms. Deepti Gupta

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Landing page: https://doi.org/10.22214/ijraset.2022.42718

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Concepts: Hallucinating, Discriminator, Computer science, Residual, Normalization (sociology), Artificial intelligence, Block (permutation group theory), Convolutional neural network, Adversarial system, Generative grammar, Computer vision, Algorithm, Mathematics, Geometry, Telecommunications, Detector, Anthropology, Sociology

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Cited by: 2

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Citations by year (recent): 2025: 1, 2024: 1

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abstract_inverted_index.unpleasant	60
abstract_inverted_index.accompanied	58
abstract_inverted_index.activation,	157
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abstract_inverted_index.hallucinated	54
abstract_inverted_index.increasingly	5
abstract_inverted_index.relativistic	132
abstract_inverted_index.architecture,	91
abstract_inverted_index.breakthroughs	36
abstract_inverted_index.convolutional	49
abstract_inverted_index.discriminator	137
abstract_inverted_index.improvements,	172
abstract_inverted_index.normalization	119
abstract_inverted_index.reconstruction	2, 17, 32
abstract_inverted_index.Super-resolution	1
abstract_inverted_index.super-resolution	16, 44
abstract_inverted_index.Residual-in-Residual	113
cited_by_percentile_year.max	95
cited_by_percentile_year.min	90
countries_distinct_count	0
institutions_distinct_count	4
sustainable_development_goals[0].id	https://metadata.un.org/sdg/10
sustainable_development_goals[0].score	0.7200000286102295
sustainable_development_goals[0].display_name	Reduced inequalities
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