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G-GANISR: Gradual generative adversarial network for image super resolution
Shamsolmoali, Pourya1; Zareapoor, Masoumeh1,2; Wang, Ruili3; Jain, Deepak Kumar4; Yang, Jie1
Source PublicationNEUROCOMPUTING
Corresponding AuthorWang, Ruili(
AbstractAdversarial methods have demonsterated to be signifiant at generating realistic images. However, these approaches have a challenging training process which partially attributed to the performance of discriminator. In this paper, we proposed an efficient super-resolution model based on generative adversarial network (GAN), to effectively generate reprehensive information and improve the test quality of the real-world images. To overcome the current issues, we designed the discriminator of our model based on the Least Square Loss function. The proposed network is organized by a gradual learning process from simple to advanced, which means from the small upsampling factors to the large upsampling factor that helps to improve the overall stability of the training. In particular, to control the model parameters and mitigate the training difficulties, dense residual learning strategy is adopted. Indeed, the key idea of proposed methodology is (i) fully exploit all the image details without losing information by gradually increases the task of discriminator, where the output of each layer is gradually improved in the next layer. In this way the model efficiently generates a super-resolution image even up to high scaling factors (e.g. x 8). (ii) The model is stable during the learning process, as we use least square loss instead of cross-entropy. In addition, the effects of different objective function on training stability are compared. To evaluate the model we conducted two sets of experiments, by using the proposed gradual GAN and the regular GAN to demonstrate the efficiency and stability of the proposed model for both quantitative and qualitative benchmarks. (C) 2019 Published by Elsevier B.
KeywordImage super-resolution Loss functions GAN CNN Gradual learning
Indexed BySCI
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000488202500014
Citation statistics
Cited Times:26[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorWang, Ruili
Affiliation1.Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai, Peoples R China
2.Tokyo Univ Technol, Dept Comp Sci, Tokyo, Japan
3.Massey Univ, Inst Nat & Math Sci, Auckland, New Zealand
4.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Shamsolmoali, Pourya,Zareapoor, Masoumeh,Wang, Ruili,et al. G-GANISR: Gradual generative adversarial network for image super resolution[J]. NEUROCOMPUTING,2019,366:140-153.
APA Shamsolmoali, Pourya,Zareapoor, Masoumeh,Wang, Ruili,Jain, Deepak Kumar,&Yang, Jie.(2019).G-GANISR: Gradual generative adversarial network for image super resolution.NEUROCOMPUTING,366,140-153.
MLA Shamsolmoali, Pourya,et al."G-GANISR: Gradual generative adversarial network for image super resolution".NEUROCOMPUTING 366(2019):140-153.
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