Progressive with Purpose: Guiding Progressive Inpainting DNNs through Context and Structure
Kangdi Shi (1), Muhammad Alrabeiah (2), Jun Chen (1) ((1), Department of Electrical, Computer Engineering, McMaster University,, Hamilton, Canada, (2) Electrical Engineering Department, King Saud, University, Saudi Arabia.)

TL;DR
This paper introduces a progressive inpainting network that uses multi-frequency feature extraction inspired by pyramids to better preserve structure and context, leading to improved image restoration results.
Contribution
The paper presents a novel GLE module and a progressive inpainting framework that effectively combines structural and contextual features across frequency components.
Findings
Outperforms state-of-the-art inpainting methods
Maintains structural and contextual integrity of images
Achieves significant performance improvements
Abstract
The advent of deep learning in the past decade has significantly helped advance image inpainting. Although achieving promising performance, deep learning-based inpainting algorithms still struggle from the distortion caused by the fusion of structural and contextual features, which are commonly obtained from, respectively, deep and shallow layers of a convolutional encoder. Motivated by this observation, we propose a novel progressive inpainting network that maintains the structural and contextual integrity of a processed image. More specifically, inspired by the Gaussian and Laplacian pyramids, the core of the proposed network is a feature extraction module named GLE. Stacking GLE modules enables the network to extract image features from different image frequency components. This ability is important to maintain structural and contextual integrity, for high frequency components…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Image Processing Techniques and Applications · Advanced Image Processing Techniques
MethodsInpainting
