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.
|Title of host publication||Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021|
|Publisher||IEEE Computer Society|
|Number of pages||9|
|Publication status||Published - 2021 Jun|
|Event||2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 - Virtual, Online, United States|
Duration: 2021 Jun 19 → 2021 Jun 25
|Name||IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops|
|Conference||2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021|
|Period||21/6/19 → 21/6/25|
Bibliographical notePublisher Copyright:
© 2021 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering