  The exposed cameras of UAV can shake, shift, or even malfunction under the
influence of harsh weather, while the add-on devices (Dupont lines) are very
vulnerable to damage.
  We can place a low-cost T-OLED overlay around the camera to protect it, but
this would also introduce image degradation issues.
  In particular, the temperature variations in the atmosphere can create mist
that adsorbs to the T-OLED, which can cause secondary disasters (i.e., more
severe image degradation) during the UAV's filming process.
  To solve the image degradation problem caused by overlaying T-OLEDs, in this
paper we propose a new method to enhance the visual experience by enhancing the
texture and color of images.
  Specifically, our method trains a lightweight network to estimate a low-rank
affine grid on the input image, and then utilizes the grid to enhance the input
image at block granularity.
  The advantages of our method are that no reference image is required and the
loss function is developed from visual experience.
  In addition, our model can perform high-quality recovery of images of
arbitrary resolution in real time.
  In the end, the limitations of our model and the collected datasets
(including the daytime and nighttime scenes) are discussed.
