A novel 3D medical image super-resolution method based on densely connected network

2020 ◽  
Vol 62 ◽  
pp. 102120
Author(s):  
Wei Lu ◽  
Zhijin Song ◽  
Jinghui Chu
Keyword(s):  
Medical Image ◽  
Super Resolution ◽  
Resolution Method ◽  
Connected Network ◽  
Image Super Resolution

scholarly journals A Fast Medical Image Super Resolution Method Based on Deep Learning Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 12319-12327 ◽  
Author(s):  
Shengxiang Zhang ◽  
Gaobo Liang ◽  
Shuwan Pan ◽  
Lixin Zheng
Keyword(s):  
Deep Learning ◽  
Medical Image ◽  
Super Resolution ◽  
Resolution Method ◽  
Learning Network ◽  
Image Super Resolution ◽  
Deep Learning Network

A trusted medical image super-resolution method based on feedback adaptive weighted dense network

2020 ◽  
Vol 106 ◽  
pp. 101857 ◽  
Author(s):  
Lihui Chen ◽  
Xiaomin Yang ◽  
Gwanggil Jeon ◽  
Marco Anisetti ◽  
Kai Liu
Keyword(s):  
Medical Image ◽  
Super Resolution ◽  
Dense Network ◽  
Resolution Method ◽  
Image Super Resolution

scholarly journals Design of Cascaded CNN for Medical Image Super Resolution

2021 ◽  
Vol 7 (3) ◽  
pp. 22-29
Author(s):  
Kajol Singh ◽  
Manish Saxena
Keyword(s):  
Neural Networks ◽  
Medical Image ◽  
Research Work ◽  
Super Resolution ◽  
Primary Objective ◽  
Lighting Conditions ◽  
Proposed Model ◽  
Filter Size ◽  
Medical Image Reconstruction ◽  
Image Super Resolution

The images captured through a camera usually belong to over or under exposed conditions. The reason may be inappropriate lighting conditions or camera resolution. Hence, it is of utmost importance to have a few enhancement techniques that could make these artefacts look better. Hence, the primary objective pertaining to the adjustment and enhancement techniques is to enhance the characteristics of an image. The initial numeric values related to an image get distorted when an image is enhanced. Therefore, enhancement techniques should be designed in such a way that the image quality isn’t compromised. This research work is focused on proposed a network design for deep convolution neural networks for application of super resolution techniques. To improve the complexity of existing techniques this work is intended towards network designs, different filter size and CNN architecture. The CNN model is most effective model for detection and segmentation in image. This model will improve the efficiency of medical image reconstruction from LR to HR. The proposed model showed its efficiency not only PET medical images but also on retinal database and achieved advance results as compared to existing works.

Medical-Image Super-Resolution Reconstruction Method Based on Residual Channel Attention Network

2020 ◽  
Vol 57 (2) ◽  
pp. 021014
Author(s):  
刘可文 Liu Kewen ◽  
马圆 Ma Yuan ◽  
熊红霞 Xiong Hongxia ◽  
严泽军 Yan Zejun ◽  
周志军 Zhou Zhijun ◽  
...  
Keyword(s):  
Medical Image ◽  
Super Resolution ◽  
Reconstruction Method ◽  
Attention Network ◽  
Image Super Resolution

Advanced visualization using image super-resolution method for three-dimensional mobile system

2021 ◽  
Vol 480 ◽  
pp. 126494
Author(s):  
Munkh-Uchral Erdenebat ◽  
Yan-Ling Piao ◽  
Ki-Chul Kwon ◽  
Moung Hee Lee ◽  
Ki Hoon Kwon ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Super Resolution ◽  
Mobile System ◽  
Resolution Method ◽  
Image Super Resolution ◽  
Advanced Visualization

A Single-Image Super-Resolution Method Based on Progressive-Iterative Approximation

2020 ◽  
Vol 22 (6) ◽  
pp. 1407-1422
Author(s):  
Yunfeng Zhang ◽  
Ping Wang ◽  
Fangxun Bao ◽  
Xunxiang Yao ◽  
Caiming Zhang ◽  
...  
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Iterative Approximation ◽  
Resolution Method ◽  
Image Super Resolution ◽  
Single Image Super Resolution

A fast single-image super-resolution method implemented with CUDA

2018 ◽  
Vol 16 (1) ◽  
pp. 81-97 ◽  
Author(s):  
Yuan Yuan ◽  
Xiaomin Yang ◽  
Wei Wu ◽  
Hu Li ◽  
Yiguang Liu ◽  
...  
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Resolution Method ◽  
Image Super Resolution ◽  
Single Image Super Resolution
Sign in / Sign up

Export Citation Format

Share Document