Semantic Image Inpainting Through Improved Wasserstein Generative Adversarial Networks

Vitoria, Patricia; Sintes, Joan; Ballester, Coloma

doi:10.48550/arxiv.1812.01071

Cited by 5 publications

(6 citation statements)

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“…PSNR (dB) SSIM SIIWGAN [22] 19.2 0.920 SIIDGM [24] 19.4 0.907 CE [20] 21.3 0.923 GL [10] 23.19 0.936 GntInp [25] 23.80 0.940 GMCNN [23] 24.46 0.944 GL+LID [15] 25.56 0.953 Ours 31.8 0.946…”

Section: Methodsmentioning

confidence: 99%

Local Facial Attribute Transfer through Inpainting

Durall¹,

Pfreundt²,

Keuper³

2020

Preprint

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The term "attribute transfer" refers to the tasks of altering images in such a way, that the semantic interpretation of a given input image is shifted towards an intended direction, which is quantified by semantic attributes. Prominent example applications are photo realistic changes of facial features and expressions, like changing the hair color, adding a smile, enlarging the nose or altering the entire context of a scene, like transforming a summer landscape into a winter panorama. Recent advances in attribute transfer are mostly based on generative deep neural networks, using various techniques to manipulate images in the latent space of the generator. In this paper, we present a novel method for the common sub-task of local attribute transfers, where only parts of a face have to be altered in order to achieve semantic changes (e.g. removing a mustache). In contrast to previous methods, where such local changes have been implemented by generating new (global) images, we propose to formulate local attribute transfers as an inpainting problem. Removing and regenerating only parts of images, our "Attribute Transfer Inpainting Generative Adversarial Network" (ATI-GAN) is able to utilize local context information, resulting in visually sound results.

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“…PSNR (dB) SSIM SIIWGAN [22] 19.2 0.920 SIIDGM [24] 19.4 0.907 CE [20] 21.3 0.923 GL [10] 23.19 0.936 GntInp [25] 23.80 0.940 GMCNN [23] 24.46 0.944 GL+LID [15] 25.56 0.953 Ours 31.8 0.946…”

Section: Methodsmentioning

confidence: 99%

Local Facial Attribute Transfer through Inpainting

Durall¹,

Pfreundt²,

Keuper³

2020

Preprint

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“…Inspired by WGAN and WGAN-GP, Vitoria et al [8] provide a novel technique for semantic image inpainting in order to restore significant lost portions of an image. The paper improves the architecture of WGAN-GP such as the removal of the fully-connected layers over the top of convolutional features, the introduction of the residual learning framework in generator as well as discriminator and the switch from the frequently used batch normalization by a layer normalization.…”

Section: Wgan-basedmentioning

confidence: 99%

“…Based on context encoder, Iizuka et al [3] divide discriminator into global and local one to ensure the consistency. Then we introduce contextual attention-based methods [4,5] and WGAN-based methods [6,7,8]. Unlike the other two ideas, WGAN-based methods use a new loss function without changing the architecture or convolution and also achieve top-tier results.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Improved GAN based Image Inpainting for Different Aims

Wang¹

2023

HSET

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Computer Vision (CV) is an important field of Artificial Intelligence (AI) that focuses on processing and analyzing the visual data input like images or videos. And image inpainting technique is a task of reconstructing image with missing parts by using the information of the existing part, which becomes a crucial part in CV. It has various image processing applications such as digital cultural heritage preservation, old photograph restoration and object removal. The researchers found that using generative adversarial network can extract the feature from image without huge amount of computation which are two main challenges of image inpainting, therefore GAN-based inpainting method has recently been a hub for research. In this paper, we review most of the latest improved image inpainting methods and divide them into three categories according to their different aims: for semantic image inpainting, for generating higher diversity and for irregular or free-form image input. Firstly, we review the methods from structure and algorithm. Then we analyze the advantages and disadvantages of each method. Finally, we conclude the challenges or limitations raised from our findings and some potential development trend.

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“…On the RBG-D images, Dhamo et al [48] use CNN and GAN model to generate the background of a scene by removing the object in the foreground image as performed by many methodsod of motion detection using background subtraction [49][50][51] . In order to complete the missing regions in the image, Vitoria et al [52] proposed an improved version of the Wasserstein GAN with the incorporation of Discriminator and Generator architecture. In the same context, but on sea surface temperature (SST) images, the Dong et al [53] proposed a Deep Convolutional Generative adversarial network (DCGAN) for filing the missing parts of the 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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Semantic Image Inpainting Through Improved Wasserstein Generative Adversarial Networks

Cited by 5 publications

References 0 publications

Local Facial Attribute Transfer through Inpainting

Local Facial Attribute Transfer through Inpainting

A Survey on Improved GAN based Image Inpainting for Different Aims

Image Inpainting: A Review

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