论文标题
详细信息或工件：一种当地歧视性学习方法，用于现实形象超级分辨率
Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution
论文作者
论文摘要
具有生成对抗网络（GAN）的单图像超分辨率（SISR）最近由于其产生丰富细节的潜力而引起了越来越多的关注。但是，对甘恩的培训是不稳定的，它通常会引入许多感知上令人不愉快的文物以及生成的细节。在本文中，我们证明可以训练基于GAN的SISR模型，该模型可以稳定地产生感知现实的细节，同时抑制视觉伪像。基于观察到，人工制品区域的局部统计数据（例如，残余方差）通常与感知友好的细节区域不同，我们开发了一个框架，以区分GAN基因生成的伪影和现实细节，因此产生了伪影地图以正规化和稳定模型训练过程。我们提出的本地歧视性学习（LDL）方法很简单却有效，可以轻松地插入现成的SISR方法并提高其性能。实验表明，LDL胜过最先进的SISR方法，不仅可以达到更高的重建精度，而且还达到了合成和现实世界数据集的卓越感知质量。代码和模型可在https://github.com/csjliang/ldl上找到。
Single image super-resolution (SISR) with generative adversarial networks (GAN) has recently attracted increasing attention due to its potentials to generate rich details. However, the training of GAN is unstable, and it often introduces many perceptually unpleasant artifacts along with the generated details. In this paper, we demonstrate that it is possible to train a GAN-based SISR model which can stably generate perceptually realistic details while inhibiting visual artifacts. Based on the observation that the local statistics (e.g., residual variance) of artifact areas are often different from the areas of perceptually friendly details, we develop a framework to discriminate between GAN-generated artifacts and realistic details, and consequently generate an artifact map to regularize and stabilize the model training process. Our proposed locally discriminative learning (LDL) method is simple yet effective, which can be easily plugged in off-the-shelf SISR methods and boost their performance. Experiments demonstrate that LDL outperforms the state-of-the-art GAN based SISR methods, achieving not only higher reconstruction accuracy but also superior perceptual quality on both synthetic and real-world datasets. Codes and models are available at https://github.com/csjliang/LDL.
