Image Inpainting Based on Generative Adversarial Networks

Liu, Huaming; Lu, Guanming; Bi, Xuehui; Yan, Jun; Wang, Weilan

doi:10.1109/fskd.2018.8686914

Cited by 14 publications

(6 citation statements)

References 18 publications

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“…A good image restoration method demands to preserve the structural consistency and the texture clarity. For this reason, Liu et al [56] proposed a GAN-based method for image inpainting on face images. FiNet [57] is another approach found in literature for fashion image inpainting that consists of completing the missing parts in fashion images.…”

Section: Gan-based Approachesmentioning

confidence: 99%

Image Inpainting: A Review

Elharrouss

Almaadeed

Al‐Maadeed

et al. 2019

Neural Process Lett

245

100

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Although image inpainting, or the art of repairing the old and deteriorated images, has been around for many years, it has gained even more popularity because of the recent development in image processing techniques. With the improvement of image processing tools and the flexibility of digital image editing, automatic image inpainting has found important applications in computer vision and has also become an important and challenging topic of research in image processing. This paper is a brief review of the existing image inpainting approaches we first present a global vision on the existing methods for image inpainting. We attempt to collect most of the existing approaches and classify them into three categories, namely, sequential-based, CNN-based and GAN-based methods. In addition, for each category, a list of methods for the different types of distortion on the images is presented. Furthermore, collect a list of the available datasets and discuss these in our paper. This is a contribution for digital image inpainting researchers trying to look for the available datasets because there is a lack of datasets available for image inpainting. As the final step in this overview, we present the results of real evaluations of the three categories of image inpainting methods performed on the datasets used, for the different types of image distortion. In the end, we also present the evaluations metrics and discuss the performance of these methods in terms of these metrics. This overview can be used as a reference for image inpainting researchers, and it can also facilitate the comparison of the methods as well as the datasets used. The main contribution of this paper is the presentation of the three categories of image inpainting methods along with a list of available datasets that the researchers can use to evaluate their proposed methodology against.

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Section: Gan-based Approachesmentioning

confidence: 99%

Image Inpainting: A Review

Elharrouss

Almaadeed

Al‐Maadeed

et al. 2019

Neural Process Lett

245

100

View full text Add to dashboard Cite

show abstract

“…However, GANs can generate similar information based on the input image, so the inpainted result is more plausible than that of a method which only uses CNNs. Reference Liu et al [21] proposed an inpainting method for faces using GANs. Since only GANs are used, the image resolution is low or tends to be unstable for training purposes.…”

Section: Single Image Inpaintingmentioning

confidence: 99%

A 360-degree Panoramic Image Inpainting Network Using a Cube Map

Han

Suh

2020

Computers, Materials &Amp; Continua

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Inpainting has been continuously studied in the field of computer vision. As artificial intelligence technology developed, deep learning technology was introduced in inpainting research, helping to improve performance. Currently, the input target of an inpainting algorithm using deep learning has been studied from a single image to a video. However, deep learning-based inpainting technology for panoramic images has not been actively studied. We propose a 360-degree panoramic image inpainting method using generative adversarial networks (GANs). The proposed network inputs a 360-degree equirectangular format panoramic image converts it into a cube map format, which has relatively little distortion and uses it as a training network. Since the cube map format is used, the correlation of the six sides of the cube map should be considered. Therefore, all faces of the cube map are used as input for the whole discriminative network, and each face of the cube map is used as input for the slice discriminative network to determine the authenticity of the generated image. The proposed network performed qualitatively better than existing single-image inpainting algorithms and baseline algorithms.

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“…These contents contain natural images, building images, facial images and many other types of images. The CelebFaces attribute dataset (CelebA) [32] is a largescale facial attribute dataset containing 202,599 face images of 10,177 celebrities, and 5 landmark locations. Each image has 40 attribute annotations, and the images cover large pose changes and background clutter.…”

Section: Datasetmentioning

confidence: 99%

A method of inpainting moles and acne on the high‐resolution face photos

Zhang

Liu

et al. 2020

IET Image Processing

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With the rapid development of mobile phones, more and more high‐resolution photos are taken. The demand for high‐resolution image inpainting is becoming increasingly urgent. In order to repair high‐resolution face images automatically and quickly, this paper proposes an improved generative adversarial networks method. Firstly, we made a high‐resolution dataset for training and testing, and abandoned the traditional 256×256 size data. Secondly, since the existing methods can only repair the mask with fixed size and shape on the image, when the global average pooling layer is used in the network, the improved network can repair the moles and acne with arbitrary sizes and shapes on the human face photos. Finally, in order to achieve optimal performance of the network, a mixed loss function is used in training. The experimental results prove that our method has not only achieved good results in qualitative results, but also achieved excellent results in quantitative results.

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Image Inpainting Based on Generative Adversarial Networks

Cited by 14 publications

References 18 publications

Image Inpainting: A Review

Image Inpainting: A Review

A 360-degree Panoramic Image Inpainting Network Using a Cube Map

A method of inpainting moles and acne on the high‐resolution face photos

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