Understanding the reconstruction loss in the paper "Anomaly Detection using Deep Learning based Image Completion" I would like to implement the approach represented in this paper. Here they used following reconstruction loss:

$$
L(X)= \frac{\lambda \cdot || M \odot (X - F(\overline{M} \odot X)) ||_{1} + (1 - \lambda) \cdot || \overline{M} \odot (X - F(\overline{M} \odot X)) ||_{1}}{N}
$$

Unfortunately, the author does not explain the function $F$.
Does someone know a similar function or could understand the function's purpose from the context?
$F$ in this context is the output of the Convolutional Neural Network that's being trained, which is of the same size as $X$.