FRGAN: A Blind Face Restoration with Generative Adversarial Networks

Wei, Tongxin; Li, Qingbao; Chen, Zhifeng; Liu, Jinjin

doi:10.1155/2021/2384435

Cited by 2 publications

(1 citation statement)

References 45 publications

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“…Not only in Light it can even works in complete dark mode It is worst in case of different scenarios [9] Postural Transformer Network and Face Generative Adversarial Networks Reconstruction speed is fast Reconstruction speed is fast [10] Semi-Cycled Generative Adversarial Networks (SCGAN)…”

Section: Effective Optimization and Improved Clinical Diagnostic Valuementioning

confidence: 99%

Enhancing Upscaled Image Resolution Using Hybrid Generative Adversarial Network-Enabled Frameworks

Geetha,

Jebamalar,

Shiney

et al. 2024

IEEE Access

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Upscaling images typically depends on facial features, such as facial geometry details or references, to rebuild reasonable details. The low-quality input images cannot offer accurate geometric details, and the high-quality details are obscure, limiting performance in practical conditions. This work addresses the problem using an enhanced version of the generative adversarial network (GAN) model that uses rich and varied facial features incorporated into the pretrained StyleGAN2 for face restoration. The generated facial features are integrated into the facial restoration process using spatial feature transform layers to achieve facial details and color quality to improve reliability. In particular, the image background is upsampled using a modified enhanced super-resolution GAN trained in parallel to remove noise while recreating a high-resolution image from low-quality input. The upscale image resolution GAN is used to enlarge the image for a clear view without sacrificing the original image quality. Finally, by combining the upscaled background and restored faces, the hybrid GAN-enabled framework can obtain high-resolution upscaled images. An experimental comparison has been conducted using the Flickr-Faces-HQ Dataset collected from Kaggle. The findings indicate that the suggested framework outperforms existing methods in terms of reconstruction, adversarial, and facial component loss metrics as well as similarity indexes.

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Section: Effective Optimization and Improved Clinical Diagnostic Valuementioning

confidence: 99%

Enhancing Upscaled Image Resolution Using Hybrid Generative Adversarial Network-Enabled Frameworks

Geetha,

Jebamalar,

Shiney

et al. 2024

IEEE Access

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Vision Transformers in Image Restoration: A Survey

Ali

Benjdira

Koubâa

et al. 2023

Sensors

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The Vision Transformer (ViT) architecture has been remarkably successful in image restoration. For a while, Convolutional Neural Networks (CNN) predominated in most computer vision tasks. Now, both CNN and ViT are efficient approaches that demonstrate powerful capabilities to restore a better version of an image given in a low-quality format. In this study, the efficiency of ViT in image restoration is studied extensively. The ViT architectures are classified for every task of image restoration. Seven image restoration tasks are considered: Image Super-Resolution, Image Denoising, General Image Enhancement, JPEG Compression Artifact Reduction, Image Deblurring, Removing Adverse Weather Conditions, and Image Dehazing. The outcomes, the advantages, the limitations, and the possible areas for future research are detailed. Overall, it is noted that incorporating ViT in the new architectures for image restoration is becoming a rule. This is due to some advantages compared to CNN, such as better efficiency, especially when more data are fed to the network, robustness in feature extraction, and a better feature learning approach that sees better the variances and characteristics of the input. Nevertheless, some drawbacks exist, such as the need for more data to show the benefits of ViT over CNN, the increased computational cost due to the complexity of the self-attention block, a more challenging training process, and the lack of interpretability. These drawbacks represent the future research direction that should be targeted to increase the efficiency of ViT in the image restoration domain.

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FRGAN: A Blind Face Restoration with Generative Adversarial Networks

Cited by 2 publications

References 45 publications

Enhancing Upscaled Image Resolution Using Hybrid Generative Adversarial Network-Enabled Frameworks

Enhancing Upscaled Image Resolution Using Hybrid Generative Adversarial Network-Enabled Frameworks

Vision Transformers in Image Restoration: A Survey

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