Survey on leveraging pre-trained generative adversarial networks for image editing and restoration

Liu, Ming; Wei, Yuxiang; Wu, Xiaohe; Zuo, Wangmeng; Zhang, Lei

doi:10.1007/s11432-022-3679-0

Cited by 11 publications

(4 citation statements)

References 136 publications

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“…To gain interest from a greater audience on social media, posted images tend to be edited, and research on automatic image editing is becoming extremely important [4][5][6]. From the perspective of less burden in image editing and easier reflection on user intentions, textguided image editing has become an important topic.…”

Section: Text-guided Image Editingmentioning

confidence: 99%

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Text-Guided Image Editing Based on Post Score for Gaining Attention on Social Media

Watanabe,

Togo,

Maeda

et al. 2024

Sensors

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Text-guided image editing has been highlighted in the fields of computer vision and natural language processing in recent years. The approach takes an image and text prompt as input and aims to edit the image in accordance with the text prompt while preserving text-unrelated regions. The results of text-guided image editing differ depending on the way the text prompt is represented, even if it has the same meaning. It is up to the user to decide which result best matches the intended use of the edited image. This paper assumes a situation in which edited images are posted to social media and proposes a novel text-guided image editing method to help the edited images gain attention from a greater audience. In the proposed method, we apply the pre-trained text-guided image editing method and obtain multiple edited images from the multiple text prompts generated from a large language model. The proposed method leverages the novel model that predicts post scores representing engagement rates and selects one image that will gain the most attention from the audience on social media among these edited images. Subject experiments on a dataset of real Instagram posts demonstrate that the edited images of the proposed method accurately reflect the content of the text prompts and provide a positive impression to the audience on social media compared to those of previous text-guided image editing methods.

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Section: Text-guided Image Editingmentioning

confidence: 99%

“…In other words, images posted on Instagram have significant worth. To enhance the worth, there are more opportunities to edit images, and research on automatic image editing is becoming important [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Text-Guided Image Editing Based on Post Score for Gaining Attention on Social Media

Watanabe,

Togo,

Maeda

et al. 2024

Sensors

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“…This is a difficult point for the VI-REID task. At present, some research methods use Generative Adversarial Networks (GAN) [4] to convert visible light modal images into pseudo-infrared images in order to expand the training set samples and achieve better recognition results. In addition, there are some research ways to use multiple sharing layers to mine the common features between two states.…”

Section: Related Workmentioning

confidence: 99%

Mask-guided two-stream attention network with modality alignment for infrared-visible person re-identification

Gou,

Zhang,

Lan

et al. 2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

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In the existing person re-identification research, most of the studies are based on the person re-identification of visible light images, and the illumination conditions can affect the experimental results. Therefore, cross-modal person reidentification research has emerged, and the most commonly used method is visible-infrared person re-identification. Due to different cameras, different camera angles, different scenes, and other factors, cross-modal person reidentification faces great challenges of cross-modal differences and intra-class variations. Therefore, Mask-guided Twostream Attention Network (MTAN) with modality alignment for infrared-visible person re-identification is proposed in this paper. First, the mask-guided two-stream feature extraction network is proposed to extract shape-aware modality features. Then, the modality alignment module is designed to eliminate cross-modal feature distribution bias. Overall, the proposed method combines the mask-guided two-stream feature extraction network, modality alignment module and the loss functions, to effectively extract modality-aware distinguishable features and reduce cross-modal distribution bias.

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“…After an extensive literature review, it was found that generative adversarial networks (GANs) [28] have found extensive applications in the domain of image generation. Thanks to their ability to simulate complex functional relationships and their powerful generation capabilities, several papers have used GANs to process image data and have achieved good results [29][30][31][32]. Residual Network (ResNet) [33] is another highly influential network following the three classic CNNs: AlexNet, VGG, and GoogleNet.…”

Section: Introductionmentioning

confidence: 99%

Blank Strip Filling for Logging Electrical Imaging Based on Multiscale Generative Adversarial Network

Sun

Gong

et al. 2023

Processes

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The Fullbore Formation Micro Imager (FMI) represents a proficient method for examining subterranean oil and gas deposits. Despite its effectiveness, due to the inherent configuration of the borehole and the logging apparatus, the micro-resistivity imaging tool cannot achieve complete coverage. This limitation manifests as blank regions on the resulting micro-resistivity logging images, thus posing a challenge to obtaining a comprehensive analysis. In order to ensure the accuracy of subsequent interpretation, it is necessary to fill these blank strips. Traditional inpainting methods can only capture surface features of an image, and can only repair simple structures effectively. However, they often fail to produce satisfactory results when it comes to filling in complex images, such as carbonate formations. In order to address the aforementioned issues, we propose a multiscale generative adversarial network-based image inpainting method using U-Net. Firstly, in order to better fill the local texture details of complex well logging images, two discriminators (global and local) are introduced to ensure the global and local consistency of the image; the local discriminator can better focus on the texture features of the image to provide better texture details. Secondly, in response to the problem of feature loss caused by max pooling in U-Net during down-sampling, the convolution, with a stride of two, is used to reduce dimensionality while also enhancing the descriptive ability of the network. Dilated convolution is also used to replace ordinary convolution, and multiscale contextual information is captured by setting different dilation rates. Finally, we introduce residual blocks on the U-Net network in order to address the degradation problem caused by the increase in network depth, thus improving the quality of the filled logging images. The experiment demonstrates that, in contrast to the majority of existing filling algorithms, the proposed method attains superior outcomes when dealing with the images of intricate lithology.

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Survey on leveraging pre-trained generative adversarial networks for image editing and restoration

Cited by 11 publications

References 136 publications

Text-Guided Image Editing Based on Post Score for Gaining Attention on Social Media

Text-Guided Image Editing Based on Post Score for Gaining Attention on Social Media

Mask-guided two-stream attention network with modality alignment for infrared-visible person re-identification

Blank Strip Filling for Logging Electrical Imaging Based on Multiscale Generative Adversarial Network

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