More Direct and stage-wise network for Face Super Resolution

In recent years, U-net-based models have demonstrated high performance in high-magnification face super resolution (FSR) tasks and are capable of outputting more vivid super resolution (SR) images. However, this model has some drawbacks, such as the possibility of unnecessary computations and uniform block selection at all stages of the decoder. Therefore, we propose a Direct and Stage-wise (DS) Net, which improves on CTCNet [1], a U-net based model with high qualitative results. This model outperforms previous networks by eliminating encoders to reduce computational waste, and by focusing on global feature extraction in the small tensor size stage.