@inproceedings{3c8bb6430af94782a2d4061b78143e0a,
title = "GAN with Pixel and Perceptual Regularizations for Photo-Realistic Joint Deblurring and Super-Resolution",
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.",
keywords = "Contextual loss, GANs, Image deblurring, Pixel loss, Super-resolution",
author = "Yong Li and Zhenguo Yang and Xudong Mao and Yong Wang and Qing Li and Wenyin Liu and Ying Wang",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-22514-8_36",
language = "English",
isbn = "9783030225131",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "395--401",
editor = "Marina Gavrilova and Jian Chang and Thalmann, {Nadia Magnenat} and Eckhard Hitzer and Hiroshi Ishikawa",
booktitle = "Advances in Computer Graphics - 36th Computer Graphics International Conference, CGI 2019, Proceedings",
note = "36th Computer Graphics International Conference, CGI 2019 ; Conference date: 17-06-2019 Through 20-06-2019",
}