On the Benefit of Parameter-Driven Approaches For The Modeling and The Prediction of Satisfied User Ratio For Compressed Video

This study proposes an image domain restoration network for metal artifact reduction in clinical computed tomography images. Specifically, we have proposed a pool and excite module to identify the streaking artifacts in the hidden latent space via learning a sigmoidal mask and a novel gated convolution layer, which utilises the previously learned gating weights for the reduction of metal artifacts. Our formulation of gated convolution is unique and custom-made to deal with metal artifacts. Extensive experiments on real CT images show that our method accomplishes significant improvement over the current state-of-the-art methods without requiring additional data, e.g., projection data, metal trace, etc.