Improved Semantic Image Inpainting Method with Deep Convolution Generative Adversarial Networks

Big Data ◽  
2021 ◽  
Author(s):  
Xiaoning Chen ◽  
Jian Zhao
Keyword(s):  
Image Inpainting ◽  
Generative Adversarial Networks ◽  
Adversarial Networks

scholarly journals Image Inpainting Based on Generative Adversarial Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 22884-22892 ◽  
Author(s):  
Yi Jiang ◽  
Jiajie Xu ◽  
Baoqing Yang ◽  
Jing Xu ◽  
Junwu Zhu
Keyword(s):  
Image Inpainting ◽  
Generative Adversarial Networks ◽  
Adversarial Networks

Attentional coarse-and-fine generative adversarial networks for image inpainting

2020 ◽  
Vol 405 ◽  
pp. 259-269 ◽  
Author(s):  
Minyu Chen ◽  
Zhi Liu ◽  
Linwei Ye ◽  
Yang Wang
Keyword(s):  
Image Inpainting ◽  
Generative Adversarial Networks ◽  
Adversarial Networks

scholarly journals Generative Adversarial Networks for Facial Image Inpainting and Super-Resolution

2021 ◽  
Vol 2070 (1) ◽  
pp. 012103
Author(s):  
G Aishwarya ◽  
K Raghesh Krishnan
Keyword(s):  
Image Inpainting ◽  
Super Resolution ◽  
Network Models ◽  
Learning Technologies ◽  
Generative Adversarial Networks ◽  
Visual Images ◽  
Network Efficiency ◽  
Second Stage ◽  
Adversarial Networks ◽  
Performance Artists

Abstract Inpainting helps to fill in the lost data in visual images. Inpainting techniques also refer to unusual image editing in distorted regions. These include areas that are noisy, blurred and watery areas. The most appropriate pixel values must be replaced in these regions to achieve good performance. Artists used to play it, and still now, pieces that are not in the picture can be inpainted in the same manner, though it takes more time. In the present age of automation, inpainting can be automated to obtain quicker and better outcomes by deep learning technologies. In this area, many of the latest techniques have been created, however, many methods produce blurred findings and data loss. Two adversarial networks are used to achieve this task, where first network aims at inpainting and the second network aims at super-resolution. The input generated as a part of first stage network is passed on to the second stage super-resolution network to overcome blurriness that is caused in the initial inpainting network. The network efficiency is determined in terms of increased PSNR obtained which is 28.19 dB with less training period of approximately 14 hours in comparison with other network models which performs similar task.

Restoration of Seismic Data Using Inpainting and EdgeConnect

2021 ◽  
Author(s):  
Marko Radosavljevic ◽  
Mikhail Naugolnov ◽  
Milos Bozic ◽  
Roman Sukhanov
Keyword(s):  
Seismic Data ◽  
Western Siberia ◽  
Image Inpainting ◽  
Seismic Exploration ◽  
Generative Adversarial Networks ◽  
Percentage Error ◽  
Accurate Estimation ◽  
Modern Approach ◽  
Adversarial Networks ◽  
Commercial Purpose

Abstract Missing seismic data is largely present problem in the world. Lack of seismic data usually occurs due to some form of natural obstacle or legislative prohibitions of seismic exploration. Restoration of seismic data would allow locating of new oil traps and reduce the risk of unsuccessful drillings. The approach is based on deep learning (image inpainting) techniques, which will be applied on inline and crossline sections of a given 3-d seismic cube, in order to restore missing parts of sections. The study was provided for non-commercial purpose for the aims of scientific research. Data used in our experiments comes from open source typical Western Siberia field. Our approach uses Generative Adversarial Networks (GANs) for completing missing parts of images (sections), based on known parts. Method can be used for restoration of arbitrarily-shaped missing parts of seismic cube, but also for extrapolation purposes. Metrics used for model evaluation are correlation coefficient and mean absolute percentage error (MAPE) between original and inpainted parts of data. This paper applies modern approach from growing image inpainting field to restore missing data, even if it's irregularly-shaped and very large. Using very powerful GANs is what gives this model ability to learn difficult inpainting scenarios, but also implicates challenging and time-consuming training process. Accurate estimation of model performances in different scenarios provides an exact instruction manual for a geologist, which helps him to identify cases where our model should be applied.

scholarly journals Super-Resolution and Inpainting with Degraded and Upgraded Generative Adversarial Networks

2020 ◽  
Author(s):  
Yawen Huang ◽  
Feng Zheng ◽  
Danyang Wang ◽  
Junyu Jiang ◽  
Xiaoqian Wang ◽  
...  
Keyword(s):  
Medical Image ◽  
Missing Values ◽  
Brain Mri ◽  
Image Inpainting ◽  
Super Resolution ◽  
Poor Performance ◽  
Independent Learning ◽  
Generative Adversarial Networks ◽  
Image Degradation ◽  
Adversarial Networks

Image super-resolution (SR) and image inpainting are two topical problems in medical image processing. Existing methods for solving the problems are either tailored to recovering a high-resolution version of the low-resolution image or focus on filling missing values, thus inevitably giving rise to poor performance when the acquisitions suffer from multiple degradations. In this paper, we explore the possibility of super-resolving and inpainting images to handle multiple degradations and therefore improve their usability. We construct a unified and scalable framework to overcome the drawbacks of propagated errors caused by independent learning. We additionally provide improvements over previously proposed super-resolution approaches by modeling image degradation directly from data observations rather than bicubic downsampling. To this end, we propose HLH-GAN, which includes a high-to-low (H-L) GAN together with a low-to-high (L-H) GAN in a cyclic pipeline for solving the medical image degradation problem. Our comparative evaluation demonstrates that the effectiveness of the proposed method on different brain MRI datasets. In addition, our method outperforms many existing super-resolution and inpainting approaches.

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