Image-to-image translation using a relativistic generative adversarial network

Xing, Xingrun; Zhang, Dawei

doi:10.1117/12.2540182

Cited by 2 publications

(2 citation statements)

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“…Different from literature [16], we replace the standard discriminator with the relativistic average discriminator (RaD) [22] to provide more target details, which can be represented by Ra D . In general, the standard discriminator st D describes whether the superresolution (SR) ISAR image is real or fake, and the relativistic average discriminator Ra D estimates the probability that a HR ISAR image is more realistic than a SR ISAR image.…”

Section: Framework Of the Proposed Ganmentioning

confidence: 99%

“…where E represents taking average value, p train I HR is the distribution of HR ISAR images, and p G I LR is the distribution of LR ISAR images. According to this criterion, adversarial loss is introduced into L G , which improves the ability to recover weak scatter points correctly for G. Different from literature [16], we replace the standard discriminator with the relativistic average discriminator (RaD) [22] to provide more target details, which can be represented by D Ra . In general, the standard discriminator D st describes whether the super-resolution (SR) ISAR image is real or fake, and the relativistic average discriminator D Ra estimates the probability that a HR ISAR image is more realistic than a SR ISAR image.…”

Section: Framework Of the Proposed Ganmentioning

confidence: 99%

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ISAR Resolution Enhancement Method Exploiting Generative Adversarial Network

Wang

Lu³

et al. 2022

Remote Sensing

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Deep learning has been used in inverse synthetic aperture radar (ISAR) imaging to improve resolution performance, but there still exist some problems: the loss of weak scattering points, over-smoothed imaging results, and the universality and generalization. To address these problems, an ISAR resolution enhancement method of exploiting a generative adversarial network (GAN) is proposed in this paper. We adopt a relativistic average discriminator (RaD) to enhance the ability of the network to describe target details. The proposed loss function is composed of feature loss, adversarial loss, and absolute loss. The feature loss is used to get the main characteristics of the target. The adversarial loss ensures that the proposed GAN recovers more target details. The absolute loss is adopted to make the imaging results not over-smoothed. Experiments based on simulated and measured data under different conditions demonstrate that the proposed method has good imaging performance. In addition, the universality and generalization of the proposed GAN are also well verified.

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Section: Framework Of the Proposed Ganmentioning

confidence: 99%

Section: Framework Of the Proposed Ganmentioning

confidence: 99%

ISAR Resolution Enhancement Method Exploiting Generative Adversarial Network

Wang

Lu³

et al. 2022

Remote Sensing

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Crack Detection Method for Engineered Bamboo Based on Super-Resolution Reconstruction and Generative Adversarial Network

Zhou

Liu

et al. 2022

Forests

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Engineering bamboo is a type of cheap and good-quality, easy-to-process material, which is widely used in construction engineering, bridge engineering, water conservancy engineering and other fields; however, crack defects lead to reduced reliability of the engineered bamboo. Accurate identification of the crack tip position and crack propagation length can improve the reliability of the engineered bamboo. Digital image correlation technology and high-quality images have been used to measure the crack tip damage zone of engineered bamboo, but the improvement of image quality with more-advanced optical equipment is limited. In this paper, we studied an application based on deep learning providing a super-resolution reconstruction method in the field of engineered bamboo DIC technology. The attention-dense residual and generative adversarial network (ADRAGAN) model was trained using a comprehensive loss function, where network interpolation was used to balance the network parameters to suppress artifacts. Compared with the super resolution generative adversarial network (SRGAN),super resolution ResNet (SRResNet), and bicubic B-spline interpolation, the superiority of the ADRAGAN network in super-resolution reconstruction of engineered bamboo speckle images was verified through assessment of both objective evaluation indices (PSNR and SSIM) and a subjective evaluation index (MOS). Finally, the images generated by each algorithm were imported into the DIC analysis software, and the crack propagation length was calculated and compared. The obtained results indicate that the proposed ADRAGAN method can reconstruct engineered bamboo speckle images with high quality, obtaining a crack detection accuracy of 99.65%.

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Image-to-image translation using a relativistic generative adversarial network

Cited by 2 publications

References 1 publication

ISAR Resolution Enhancement Method Exploiting Generative Adversarial Network

ISAR Resolution Enhancement Method Exploiting Generative Adversarial Network

Crack Detection Method for Engineered Bamboo Based on Super-Resolution Reconstruction and Generative Adversarial Network

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