SRFlow-DA: Super-Resolution Using Normalizing Flow with Deep Convolutional Block

Multiple high-resolution (HR) images can be generated from a single low-resolution (LR) image, as super-resolution (SR) is an underdetermined problem. Recently, the conditional normalizing flow-based model, SRFlow, shows remarkable performance by learning an exact map-ping from HR image manifold to a latent space. The flow-based SR model allows sampling multiple output images from a learned SR space with a given LR image. In this work, we propose SRFlow-DA which has a more suitable architecture for the SR task based on the original SRFlow model. Specifically, our approach enlarges the receptive field by stacking more convolutional layers in the affine couplings, and so our model can get more expressive power. At the same time, we reduce the total number of model pa-rameters for efficiency. Compared to SRFlow, our SRFlow-DA achieves better or comparable PSNR and LPIPS for × 4 and ×8 SR tasks, while having a reduced number of parameters. In addition, our method generates visually clear results without excessive sharpness artifacts.