Scale-Recurrent Multi-residual Dense Network for Image Super-Resolution

2019 ◽  
pp. 132-149 ◽  
Author(s):  
Kuldeep Purohit ◽  
Srimanta Mandal ◽  
A. N. Rajagopalan
Keyword(s):  
Super Resolution ◽  
Dense Network ◽  
Image Super Resolution

Aerial image super-resolution based on deep recursive dense network for disaster area surveillance

2021 ◽  
Author(s):  
Feiqiang Liu ◽  
Qiang Yu ◽  
Lihui Chen ◽  
Gwanggil Jeon ◽  
Marcelo Keese Albertini ◽  
...  
Keyword(s):  
Super Resolution ◽  
Aerial Image ◽  
Dense Network ◽  
Disaster Area ◽  
Image Super Resolution ◽  
Area Surveillance

Deep Intelligent Neural Network for Medical Geographic Small-target Intelligent Satellite Image Super-resolution

2021 ◽  
Author(s):  
Zikang Wei ◽  
Yunqing Liu
Keyword(s):  
Neural Network ◽  
Satellite Images ◽  
Satellite Image ◽  
Super Resolution ◽  
Small Target ◽  
Dense Network ◽  
Laplacian Pyramid ◽  
Residual Structure ◽  
Image Super Resolution ◽  
Small Targets

In the field of single-image super-resolution (SISR) research, neural networks and deep learning methods are gradually being widely used by researchers. Over time, the fields of application have expanded in scope. The SISR method is also applied in the field of intelligent satellite imagery. In recent years, research applications based on intelligent satellite images have mostly focused on imaging, classification, and segmentation. They have rarely been used in actual observation problems. This article proposes a new intelligent neural network model, the Laplacian pyramid residual dense network, for the super-resolution of hyperspectral satellite medical geographic small-targets. This study proceeds in three steps. First, the three-layer Laplacian pyramid structure is designed to increase the depth of the image at the feature extraction stage. Second, the residual mode is improved and updated; a new residual block is proposed for constructing the residual dense network to enhance the feature details of the image during the training process. In the third step, an end-to-end network is established directly through the residual structure for eliminating unnecessary visualization during the process and for ease of training. According to the experimental results, it has been proved that the deep intelligent neural network method proposed here has achieved good results in the application for super-resolution of medical geographic small-target intelligent satellite images.

Accurate and Efficient Image Super-Resolution via Global-Local Adjusting Dense Network

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Xinyan Zhang ◽  
Peng Gao ◽  
Sunxiangyu Liu ◽  
Kongya Zhao ◽  
Guitao Li ◽  
...  
Keyword(s):  
Super Resolution ◽  
Dense Network ◽  
Image Super Resolution

scholarly journals Enhanced Single Image Super Resolution Method Using Lightweight Multi-Scale Channel Dense Network

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3351
Author(s):  
Yooho Lee ◽  
Dongsan Jun ◽  
Byung-Gyu Kim ◽  
Hunjoo Lee
Keyword(s):  
Super Resolution ◽  
Perceptual Quality ◽  
Dense Network ◽  
Single Image ◽  
Resolution Method ◽  
Multi Scale ◽  
Training Images ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Sr Method

Super resolution (SR) enables to generate a high-resolution (HR) image from one or more low-resolution (LR) images. Since a variety of CNN models have been recently studied in the areas of computer vision, these approaches have been combined with SR in order to provide higher image restoration. In this paper, we propose a lightweight CNN-based SR method, named multi-scale channel dense network (MCDN). In order to design the proposed network, we extracted the training images from the DIVerse 2K (DIV2K) dataset and investigated the trade-off between the SR accuracy and the network complexity. The experimental results show that the proposed method can significantly reduce the network complexity, such as the number of network parameters and total memory capacity, while maintaining slightly better or similar perceptual quality compared to the previous methods.

scholarly journals RBDN: Residual Bottleneck Dense Network for Image Super-Resolution

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Zeyu An ◽  
Junyuan Zhang ◽  
Ziyu Sheng ◽  
Xuanhe Er ◽  
Junjie Lv
Keyword(s):  
Super Resolution ◽  
Dense Network ◽  
Image Super Resolution

scholarly journals An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution

2019 ◽  
Vol 12 (2) ◽  
pp. 1592 ◽  
Author(s):  
Wang Wei ◽  
Jiang Yongbin ◽  
Luo Yanhong ◽  
Li Ji ◽  
Wang Xin ◽  
...  
Keyword(s):  
Super Resolution ◽  
Dense Network ◽  
Image Super Resolution

Medical image super-resolution with laplacian dense network

2020 ◽  
Author(s):  
Rui Tang ◽  
Lihui Chen ◽  
Rongzhu Zhang ◽  
Awais Ahmad ◽  
Marcelo Keese Albertini ◽  
...  
Keyword(s):  
Medical Image ◽  
Super Resolution ◽  
Dense Network ◽  
Image Super Resolution
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