Published on Mon Dec 23 2019

Image Outpainting and Harmonization using Generative Adversarial Networks

Basile Van Hoorick

Two methods are proposed that aim to instigate this line of research. The first approach uses a context encoder inspired by common inpainting paradigms. The second approach adds an extra post-processing step using a single-image generative model.

0
0
0
Abstract

Although the inherently ambiguous task of predicting what resides beyond all four edges of an image has rarely been explored before, we demonstrate that GANs hold powerful potential in producing reasonable extrapolations. Two outpainting methods are proposed that aim to instigate this line of research: the first approach uses a context encoder inspired by common inpainting architectures and paradigms, while the second approach adds an extra post-processing step using a single-image generative model. This way, the hallucinated details are integrated with the style of the original image, in an attempt to further boost the quality of the result and possibly allow for arbitrary output resolutions to be supported.

Thu May 14 2020
Computer Vision
Enhanced Residual Networks for Context-based Image Outpainting
Deep models struggle to understand context and extrapolation through retained information. Current models use generative adversarial networks to generate results which lack localized image feature consistency and appear fake. We propose two methods to improve this issue.
0
0
0
Mon Aug 19 2019
Computer Vision
Boundless: Generative Adversarial Networks for Image Extension
Image extension models have broad applications in image editing and computer graphics. Image inpainting has been extensively studied in the literature. It is challenging to directly apply state-of-the-art inPainting methods to image extension.
0
0
0
Sat Aug 25 2018
Computer Vision
Painting Outside the Box: Image Outpainting with GANs
The challenging task of image outpainting (extrapolation) has received relatively little attention in relation to its cousin, image inpainting. We present a deep learning approach based on Iizuka et al. for adversarially training a network to hallucinate past image boundaries.
0
0
0
Sat Nov 19 2016
Computer Vision
Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
We introduce a simple semi-supervised learning approach for images based on in-painting using an adversarial loss. Images with random patches removed are presented to a generator whose task is to fill in the hole. The in-painted images are then presented to a discriminator network
0
0
0
Wed Apr 08 2020
Computer Vision
Attentive Normalization for Conditional Image Generation
Traditional convolution-based generative adversarial networks synthesize images based on hierarchical local operations. It is still not sufficient for categories with complicated structures. In this paper, we characterize long-range dependence with attentive normalization (AN)
0
0
0
Tue Mar 20 2018
Computer Vision
Patch-Based Image Inpainting with Generative Adversarial Networks
The proposed PGGAN method includes a discriminator network that combines a global GAN (G-GAN) architecture with a patchGAN approach. P GGAN first shares network layers between G-GAN and patchGAN, then splits paths to produce two adversarial losses that feed the generator network.
0
0
0