  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.
