论文标题
面部超分辨率的随机属性建模
Stochastic Attribute Modeling for Face Super-Resolution
论文作者
论文摘要
当高分辨率（HR）图像降低到低分辨率（LR）图像中时，该图像将失去一些现有信息。因此，多个HR图像可以对应于LR图像。大多数现有方法都不考虑由随机属性引起的不确定性，这只能概率地推断出来。因此，预测的HR图像通常是模糊的，因为该网络试图反映单个输出图像中的所有可能性。为了克服这一局限性，本文提出了一种新型的面部超分辨率（SR）方案，以通过随机建模来探讨不确定性。具体而言，LR图像中的信息分别编码为确定性和随机属性。此外，提出了一个输入条件属性预测因子并分别训练，以预测仅从LR图像中的部分生存的随机属性。广泛的评估表明，所提出的方法成功地降低了学习过程中的不确定性，并优于现有的最新方法。
When a high-resolution (HR) image is degraded into a low-resolution (LR) image, the image loses some of the existing information. Consequently, multiple HR images can correspond to the LR image. Most of the existing methods do not consider the uncertainty caused by the stochastic attribute, which can only be probabilistically inferred. Therefore, the predicted HR images are often blurry because the network tries to reflect all possibilities in a single output image. To overcome this limitation, this paper proposes a novel face super-resolution (SR) scheme to take into the uncertainty by stochastic modeling. Specifically, the information in LR images is separately encoded into deterministic and stochastic attributes. Furthermore, an Input Conditional Attribute Predictor is proposed and separately trained to predict the partially alive stochastic attributes from only the LR images. Extensive evaluation shows that the proposed method successfully reduces the uncertainty in the learning process and outperforms the existing state-of-the-art approaches.
