Stochastic Attribute Modeling for Face Super-Resolution
This addresses the issue of uncertainty in face super-resolution for computer vision applications, though it is incremental as it builds on existing methods by adding stochastic modeling.
The paper tackled the problem of blurry outputs in face super-resolution by modeling stochastic attributes to account for uncertainty, resulting in a method that outperforms state-of-the-art approaches.
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.