Best-Buddy GANs for Highly Detailed Image Super-Resolution

• URL: http://arxiv.org/abs/2103.15295v1
• Date: Mon, 29 Mar 2021 02:58:27 GMT
• Title: Best-Buddy GANs for Highly Detailed Image Super-Resolution
• Authors: Wenbo Li, Kun Zhou, Lu Qi, Liying Lu, Nianjuan Jiang, Jiangbo Lu, Jiaya Jia
• Abstract summary: We consider the single image super-resolution (SISR) problem, where a high-resolution (HR) image is generated based on a low-resolution (LR) input. Most methods along this line rely on a predefined single-LR-single-HR mapping, which is not flexible enough for the SISR task. We propose best-buddy GANs (Beby-GAN) for rich-detail SISR. Relaxing the immutable one-to-one constraint, we allow the estimated patches to dynamically seek the best supervision.
• Score: 71.13466303340192
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider the single image super-resolution (SISR) problem, where a high-resolution (HR) image is generated based on a low-resolution (LR) input. Recently, generative adversarial networks (GANs) become popular to hallucinate details. Most methods along this line rely on a predefined single-LR-single-HR mapping, which is not flexible enough for the SISR task. Also, GAN-generated fake details may often undermine the realism of the whole image. We address these issues by proposing best-buddy GANs (Beby-GAN) for rich-detail SISR. Relaxing the immutable one-to-one constraint, we allow the estimated patches to dynamically seek the best supervision during training, which is beneficial to producing more reasonable details. Besides, we propose a region-aware adversarial learning strategy that directs our model to focus on generating details for textured areas adaptively. Extensive experiments justify the effectiveness of our method. An ultra-high-resolution 4K dataset is also constructed to facilitate future super-resolution research.

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