Fig. 3.

Network architectures of different learning paradigms. $E_{I^a}^c$, $E_{I^a}^a$ denote the encoders that respectively extract content and artifact features from artifact-affected images. E_I is the encoder that extracts content features from the artifact-free images. G_I and $G_{I^a}$ represent the decoders that output the artifact-free/artifact-corrected and artifact-affected images respectively. The combinations of $E_{I^a}^c\to G_I$, $E_{I^a}^a\to G_{I^a}$ or $E_I\to G_{I^a}$, and E_I → G_I construct artifact-corrected, artifact-affected, and artifact-free branches respectively. Conv denotes a convolutional layer. Note that in sub-figures (a) and (b) $E_I\to G_{I^a}$ is further followed by $E_{I^a}^c\to G_I$ to remove the added metal artifacts with the self-reduction loss [17].