Despite recent advances in technologies on single image super-resolution using deep neural networks, the key question still remains how to recover finer textures and edges. To solve this super-resolution problem, many recent researches have been using conditional generative adversarial network. However, restoring high resolution images using conditional generative adversarial network is disadvantageous in expressing fine textures and edges because there occurs the loss of spatial and high frequency informations. In this paper, informations on images in different scales are added hierarchically by using a spatially adaptive de-normalization method. This method can restore fine textures and edges of an image by inserting different scale informations for each layers in pyramid structure. In experimental results, the efficiency of the proposed method is proved by showing better performance to restore textures and edges in high quality, comparing with other state-of-the art techniques.
|Number of pages||6|
|Journal||Transactions of the Korean Institute of Electrical Engineers|
|Publication status||Published - 2021 Feb|
Bibliographical notePublisher Copyright:
© 2021 Korean Institute of Electrical Engineers. All rights reserved.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering