您当前的位置：
首页 >
文章列表页 >
Cascade residual-optimized image super-resolution reconstruction in Transformer network
Information Sciences | 更新时间：2024-07-26
|

    • Cascade residual-optimized image super-resolution reconstruction in Transformer network

    • “In the field of image super-resolution, researchers have proposed an optimized structure based on cascaded residual Transformer networks, which effectively improves the image reconstruction effect and detail clarity.”
    • Optics and Precision Engineering   Vol. 32, Issue 12, Pages: 1902-1914(2024)

    • DOI：10.37188/OPE.20243212.1902    

      CLC： TP394.1;TH691.9

    • Received：13 December 2023，

      Revised：20 February 2024，

      Published：25 June 2024

    移动端阅览

  • LIN Jianpu,WU Zhencheng,WANG Kunfu,et al.Cascade residual-optimized image super-resolution reconstruction in Transformer network[J].Optics and Precision Engineering,2024,32(12):1902-1914. DOI： 10.37188/OPE.20243212.1902.

  •  
  •  

0

Views

165

下载量

0

CSCD

Alert me when the article has been cited
提交
Tools
Download
Export Citation
Share
Add to favorites
Add to my album

Related Articles

Progressive CNN-transformer semantic compensation network for polyp segmentation
Collaborative classification of hyperspectral and LiDAR data based on CNN-transformer
Image super-resolution reconstruction based on attention and wide-activated dense residual network
Infrared dim and small target detection network based on spatial attention mechanism
Channel attention and residual concatenation network for image super-resolution

Related Author

LI Daxiang
LI Denghui
LIU Ying
TANG Yao
WU Haibin
DAI Shiyu
WANG Aili
YUJI Iwahori

Related Institution

College of Communication and Information Engineering，Xi′an University of Posts and Telecommunication，Xi′an
Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application， College of Measurement and Control Technology and Communication Engineering， Harbin University of Science and Technology
Department of Computer Science， Chubu University
College of Electron and Information， University of Electronic Science and Technology of China，Zhongshan Institute
School of Computer Science and Technology，China University of Mining and Technology
0