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2019 | OriginalPaper | Chapter

GAN with Pixel and Perceptual Regularizations for Photo-Realistic Joint Deblurring and Super-Resolution

Authors : Yong Li, Zhenguo Yang, Xudong Mao, Yong Wang, Qing Li, Wenyin Liu, Ying Wang

Published in: Advances in Computer Graphics

Publisher: Springer International Publishing

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Abstract

In this paper, we propose a Generative Adversarial Network with Pixel and Perceptual regularizations, denoted as P²GAN, to restore single motion blurry and low-resolution images jointly into clear and high-resolution images. It is an end-to-end neural network consisting of deblurring module and super-resolution module, which repairs degraded pixels in the motion-blur images firstly, and then outputs the deblurred images and deblurred features for further reconstruction. More specifically, the proposed P²GAN integrates pixel-wise loss in pixel-level, contextual loss and adversarial loss in perceptual level simultaneously, in order to guide on deblurring and super-resolution reconstruction of the raw images that are blurry and in low-resolution, which help obtaining realistic images. Extensive experiments conducted on a real-world dataset manifest the effectiveness of the proposed approaches, outperforming the state-of-the-art models.

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Title: GAN with Pixel and Perceptual Regularizations for Photo-Realistic Joint Deblurring and Super-Resolution
Authors: Yong Li
Zhenguo Yang
Xudong Mao
Yong Wang
Qing Li
Wenyin Liu
Ying Wang
Publisher: Springer International Publishing
Book: Advances in Computer Graphics
Print ISBN: 978-3-030-22513-1

Electronic ISBN: 978-3-030-22514-8

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-22514-8_36

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