  After an artificial model background subtraction, the pixels have been
labelled as foreground and background. Previous approaches to secondary
processing the output for denoising usually use traditional methods such as the
Bayesian refinement method. In this paper, we focus on using a modified U-Net
model to approximate the result of the Bayesian refinement method and improve
the result. In our modified U-Net model, the result of background subtraction
from other models will be combined with the source image as input for learning
the statistical distribution. Thus, the losing information caused by the
background subtraction model can be restored from the source image. Moreover,
since the part of the input image is already the output of the other background
subtraction model, the feature extraction should be convenient, it only needs
to change the labels of the noise pixels. Compare with traditional methods,
using deep learning methods superiority in keeping details.
