Structure-guided Image Outpainting
Xi Wang, Weixi Cheng, and Wenliang Jia

TL;DR
This paper introduces a GAN-based image outpainting method that uses structural priors and semantic embedding to improve realism and structural consistency in extrapolated images.
Contribution
It proposes a novel deep learning framework incorporating structural priors and semantic embedding loss for improved image outpainting quality.
Findings
Enhanced structural realism in outpainted images
Improved perceptual quality with semantic embedding loss
Effective multi-phase adversarial training scheme
Abstract
Deep learning techniques have made considerable progress in image inpainting, restoration, and reconstruction in the last few years. Image outpainting, also known as image extrapolation, lacks attention and practical approaches to be fulfilled, owing to difficulties caused by large-scale area loss and less legitimate neighboring information. These difficulties have made outpainted images handled by most of the existing models unrealistic to human eyes and spatially inconsistent. When upsampling through deconvolution to generate fake content, the naive generation methods may lead to results lacking high-frequency details and structural authenticity. Therefore, as our novelties to handle image outpainting problems, we introduce structural prior as a condition to optimize the generation quality and a new semantic embedding term to enhance perceptual sanity. we propose a deep learning…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Advanced Image Processing Techniques · Digital Media Forensic Detection
MethodsInpainting
