- Tytuł:
- Deep networks for image super-resolution using hierarchical features
- Autorzy:
-
Yang, Xin
Zhang, Yifan
Zhou, Dake - Powiązania:
- https://bibliotekanauki.pl/articles/2173634.pdf
- Data publikacji:
- 2022
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
super-resolution
convolutional neural network
sub-pixel convolutional neural network
densely connected neural networks
super rozdzielczość
splotowa sieć neuronowa
subpikselowa splotowa sieć neuronowa
gęsto połączone sieci neuronowe - Opis:
- To better extract feature maps from low-resolution (LR) images and recover high-frequency information in the high-resolution (HR) images in image super-resolution (SR), we propose in this paper a new SR algorithm based on a deep convolutional neural network (CNN). The network structure is composed of the feature extraction part and the reconstruction part. The extraction network extracts the feature maps of LR images and uses the sub-pixel convolutional neural network as the up-sampling operator. Skip connection, densely connected neural networks and feature map fusion are used to extract information from hierarchical feature maps at the end of the network, which can effectively reduce the dimension of the feature maps. In the reconstruction network, we add a 3×3 convolution layer based on the original sub-pixel convolution layer, which can allow the reconstruction network to have better nonlinear mapping ability. The experiments show that the algorithm results in a significant improvement in PSNR, SSIM, and human visual effects as compared with some state-of-the-art algorithms based on deep learning.
- Źródło:
-
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 1; art. no. e139616
0239-7528 - Pojawia się w:
- Bulletin of the Polish Academy of Sciences. Technical Sciences
- Dostawca treści:
- Biblioteka Nauki
Artykuł