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Published in:
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.