超分辨率matlab代码-HAN:我们的ECCV2020论文“通过整体注意力网络实现单图像超分辨率”的PyTorch代码
超分辨率matlab代码韩
我们的ECCV
2020论文“通过整体注意力网络实现单图像超分辨率”的PyTorch代码
该存储库适用于以下论文中介绍的HAN
张玉伦，李坤鹏，李凯，王丽晨，钟斌能和付云，“通过整体注意力网络实现单图像超分辨率”，ECCV
2020，
该代码基于RCAN（PyTorch）构建并在具有Titan
X
/
1080Ti
/
Xp
GPU的Ubuntu
16.04
/
18.04环境（Python3.6，PyTorch_0.4.0，CUDA8.0，cuDNN5.1）上进行了测试。
内容
介绍
信息功能在单图像超分辨率任务中起着至关重要的作用。
事实证明，渠道关注对于保留每一层中信息量丰富的功能是有效的。
但是，频道注意力将每个卷积层视为一个单独的过程，从而错过了不同层之间的相关性。
为了解决这个问题，我们提出了一个新的整体注意网络（HAN），该网络由一个图层注意模块（LAM）和一个通道空间注意模块（CSAM）组成，以对图层，通道和位置之间的整体相互依赖性进行建模。
具体地，提出的LAM通过考虑各层之间的相关性来自适应地强调分层特征。
同时，CSAM学习每个通道所有
文件下载
资源详情
[{"title":"（ 93 个子文件 15.31MB ） 超分辨率matlab代码-HAN:我们的ECCV2020论文“通过整体注意力网络实现单图像超分辨率”的PyTorch代码","children":[{"title":"HAN-master","children":[{"title":"experiment","children":[{"title":".gitignore <span style='color:#111;'> 27B </span>","children":null,"spread":false}],"spread":true},{"title":"src","children":[{"title":"loss","children":[{"title":"adversarial.py <span style='color:#111;'> 4.29KB </span>","children":null,"spread":false},{"title":"vgg.py <span style='color:#111;'> 1.08KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 4.69KB </span>","children":null,"spread":false},{"title":"discriminator.py <span style='color:#111;'> 1.56KB </span>","children":null,"spread":false}],"spread":true},{"title":"dataloader.py <span style='color:#111;'> 5.14KB </span>","children":null,"spread":false},{"title":"model","children":[{"title":"han.py <span style='color:#111;'> 8.09KB </span>","children":null,"spread":false},{"title":"edsr.py <span style='color:#111;'> 2.96KB </span>","children":null,"spread":false},{"title":"rdn.py <span style='color:#111;'> 3.13KB </span>","children":null,"spread":false},{"title":"rcan1.py <span style='color:#111;'> 17.53KB </span>","children":null,"spread":false},{"title":"rcan.py <span style='color:#111;'> 7.17KB </span>","children":null,"spread":false},{"title":"ops.py <span style='color:#111;'> 12.53KB </span>","children":null,"spread":false},{"title":"rcan4.py <span style='color:#111;'> 8.42KB </span>","children":null,"spread":false},{"title":"rdn1.py <span style='color:#111;'> 4.46KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 6.34KB </span>","children":null,"spread":false},{"title":"matrixmodel.py <span style='color:#111;'> 73.89KB </span>","children":null,"spread":false},{"title":"vdsr.py <span style='color:#111;'> 1.25KB </span>","children":null,"spread":false},{"title":"rdn2.py <span style='color:#111;'> 4.60KB </span>","children":null,"spread":false},{"title":"rcan3.py <span style='color:#111;'> 22.04KB </span>","children":null,"spread":false},{"title":"mdsr.py <span style='color:#111;'> 1.88KB </span>","children":null,"spread":false},{"title":"ddbpn.py <span style='color:#111;'> 3.54KB </span>","children":null,"spread":false},{"title":"dcn","children":[{"title":"src","children":[{"title":"deform_conv_cuda_kernel.cu <span style='color:#111;'> 41.28KB </span>","children":null,"spread":false},{"title":"deform_conv_cuda.cpp <span style='color:#111;'> 28.55KB </span>","children":null,"spread":false}],"spread":false},{"title":"__init__.py <span style='color:#111;'> 306B </span>","children":null,"spread":false},{"title":"deform_conv.py <span style='color:#111;'> 11.95KB </span>","children":null,"spread":false},{"title":"setup.py <span style='color:#111;'> 711B </span>","children":null,"spread":false}],"spread":false},{"title":"common.py <span style='color:#111;'> 2.72KB </span>","children":null,"spread":false}],"spread":false},{"title":"main.py <span style='color:#111;'> 835B </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"option.py <span style='color:#111;'> 7.52KB </span>","children":null,"spread":false},{"title":"dataset","children":[{"title":"X4","children":[{"title":"Set20","children":[{"title":"LR","children":[{"title":"X4","children":[{"title":"007000_LR.png <span style='color:#111;'> 32.68KB </span>","children":null,"spread":false},{"title":"000000_LR.png <span style='color:#111;'> 46.90KB </span>","children":null,"spread":false},{"title":"000700_LR.png <span style='color:#111;'> 40.04KB </span>","children":null,"spread":false},{"title":"000500_LR.png <span style='color:#111;'> 56.05KB </span>","children":null,"spread":false},{"title":"000100_LR.png <span style='color:#111;'> 57.07KB </span>","children":null,"spread":false},{"title":"006000_LR.png <span style='color:#111;'> 51.73KB </span>","children":null,"spread":false},{"title":"002000_LR.png <span style='color:#111;'> 50.53KB </span>","children":null,"spread":false},{"title":"005000_LR.png <span style='color:#111;'> 55.09KB </span>","children":null,"spread":false},{"title":"000800_LR.png <span style='color:#111;'> 44.76KB </span>","children":null,"spread":false},{"title":"000400_LR.png <span style='color:#111;'> 67.10KB </span>","children":null,"spread":false},{"title":"003000_LR.png <span style='color:#111;'> 30.28KB </span>","children":null,"spread":false},{"title":"004000_LR.png <span style='color:#111;'> 54.17KB </span>","children":null,"spread":false},{"title":"008000_LR.png <span style='color:#111;'> 50.57KB </span>","children":null,"spread":false},{"title":"000300_LR.png <span style='color:#111;'> 42.56KB </span>","children":null,"spread":false},{"title":"000600_LR.png <span style='color:#111;'> 35.38KB </span>","children":null,"spread":false},{"title":"009000_LR.png <span style='color:#111;'> 61.34KB </span>","children":null,"spread":false},{"title":"000200_LR.png <span style='color:#111;'> 35.69KB </span>","children":null,"spread":false},{"title":"000900_LR.png <span style='color:#111;'> 71.22KB </span>","children":null,"spread":false},{"title":"010000_LR.png <span style='color:#111;'> 46.62KB </span>","children":null,"spread":false},{"title":"001000_LR.png <span style='color:#111;'> 37.92KB </span>","children":null,"spread":false}],"spread":false}],"spread":true},{"title":"HR","children":[{"title":"000300_HR.png <span style='color:#111;'> 560.75KB </span>","children":null,"spread":false},{"title":"000900_HR.png <span style='color:#111;'> 876.55KB </span>","children":null,"spread":false},{"title":"000700_HR.png <span style='color:#111;'> 550.02KB </span>","children":null,"spread":false},{"title":"010000_HR.png <span style='color:#111;'> 664.52KB </span>","children":null,"spread":false},{"title":"000500_HR.png <span style='color:#111;'> 657.16KB </span>","children":null,"spread":false},{"title":"005000_HR.png <span style='color:#111;'> 650.09KB </span>","children":null,"spread":false},{"title":"008000_HR.png <span style='color:#111;'> 523.51KB </span>","children":null,"spread":false},{"title":"003000_HR.png <span style='color:#111;'> 671.81KB </span>","children":null,"spread":false},{"title":"001000_HR.png <span style='color:#111;'> 452.00KB </span>","children":null,"spread":false},{"title":"000200_HR.png <span style='color:#111;'> 455.86KB </span>","children":null,"spread":false},{"title":"007000_HR.png <span style='color:#111;'> 517.62KB </span>","children":null,"spread":false},{"title":"000600_HR.png <span style='color:#111;'> 457.90KB </span>","children":null,"spread":false},{"title":"000800_HR.png <span style='color:#111;'> 601.47KB </span>","children":null,"spread":false},{"title":"004000_HR.png <span style='color:#111;'> 655.64KB </span>","children":null,"spread":false},{"title":"000000_HR.png <span style='color:#111;'> 591.07KB </span>","children":null,"spread":false},{"title":"002000_HR.png <span style='color:#111;'> 680.28KB </span>","children":null,"spread":false},{"title":"009000_HR.png <span style='color:#111;'> 865.96KB </span>","children":null,"spread":false},{"title":"006000_HR.png <span style='color:#111;'> 872.14KB </span>","children":null,"spread":false},{"title":"000100_HR.png <span style='color:#111;'> 627.69KB </span>","children":null,"spread":false},{"title":"000400_HR.png <span style='color:#111;'> 814.49KB </span>","children":null,"spread":false}],"spread":false}],"spread":true},{"title":"DIV2K","children":[{"title":"TrainHR","children":[{"title":"000003_HR.png <span style='color:#111;'> 469.44KB </span>","children":null,"spread":false},{"title":"000002_HR.png <span style='color:#111;'> 721.74KB </span>","children":null,"spread":false},{"title":"000001_HR.png <span style='color:#111;'> 550.02KB </span>","children":null,"spread":false}],"spread":true},{"title":"TrainLR","children":[{"title":"000002_LR.png <span style='color:#111;'> 62.75KB </span>","children":null,"spread":false},{"title":"000003_LR.png <span style='color:#111;'> 40.51KB </span>","children":null,"spread":false},{"title":"000001_LR.png <span style='color:#111;'> 32.33KB </span>","children":null,"spread":false}],"spread":true}],"spread":true}],"spread":true}],"spread":true},{"title":"videotester.py <span style='color:#111;'> 2.23KB </span>","children":null,"spread":false},{"title":"utility.py <span style='color:#111;'> 7.31KB </span>","children":null,"spread":false},{"title":"data","children":[{"title":"div2k.py <span style='color:#111;'> 1.19KB </span>","children":null,"spread":false},{"title":"div2kjpeg.py <span style='color:#111;'> 675B </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 1.93KB </span>","children":null,"spread":false},{"title":"demo.py <span style='color:#111;'> 1.05KB </span>","children":null,"spread":false},{"title":"benchmark.py <span style='color:#111;'> 1.56KB </span>","children":null,"spread":false},{"title":"sr291.py <span style='color:#111;'> 180B </span>","children":null,"spread":false},{"title":"srdata.py <span style='color:#111;'> 5.39KB </span>","children":null,"spread":false},{"title":"video.py <span style='color:#111;'> 1.18KB </span>","children":null,"spread":false},{"title":"common.py <span style='color:#111;'> 1.74KB </span>","children":null,"spread":false}],"spread":true},{"title":"template.py <span style='color:#111;'> 1.99KB </span>","children":null,"spread":false},{"title":"demo.sh <span style='color:#111;'> 4.79KB </span>","children":null,"spread":false},{"title":"trainer.py <span style='color:#111;'> 4.85KB </span>","children":null,"spread":false}],"spread":false},{"title":"LICENSE <span style='color:#111;'> 11.09KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 4.31KB </span>","children":null,"spread":false},{"title":".gitignore <span style='color:#111;'> 1.76KB </span>","children":null,"spread":false}],"spread":true}],"spread":true}]
评论信息
其他资源
- elfutils-libelf-0.152-1.el6.x86_64.rpm
- 基于非局部扩散的图像去噪代码
- 《计算机工程与应用》论文写作模板
- J2EE规范（中文版）
- 《设计模式》清华大学出版社刘伟源码
- 河北省shp矢量
- 通过MOVE指令修改打开一个程序时的窗口大小，初始显示位置
- Computer Graphics with Open GL(4th) 无水印pdf
- Selenium IDE v2.9.1.1官方版
- 用C语言实现ARC圆弧插补计算
- stm32F1+gprs的IAP远程升级，app的adc数据采集GPS数据
- DPSK 调制解调系统
- 农历控件FOR delphiXE2
- 毫米波-源码
- pymata-express：纯Python Asyncio Arduino固件客户端-源码
- STM32的USART通讯
- ECCV_005.pdf
- 提取图像DN值
- Imatest介绍说明文档
- AchartEngine图表库之动态折线图(多条)
- AutoItLibrary-1.1_x64.rar
- ASP.NET网上实验室预约系统论文及毕业设计_开题报告
- 单载波频域均衡_SC_FDE_系统研究和实现
- tesserocr-2.2.2-cp36-cp36m-win_amd64.whl
- Android 锁屏解锁源代码
- hadoop 2.9.0 dll和winutils.exe文件64位和32位都有
免责申明
【只为小站】的资源来自网友分享，仅供学习研究，请务必在下载后24小时内给予删除，不得用于其他任何用途，否则后果自负。基于互联网的特殊性，【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查；无论 【只为小站】 经营者是否已进行审查，用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场，基于网友分享，根据中国法律《信息网络传播权保护条例》第二十二条之规定，若资源存在侵权或相关问题请联系本站客服人员，zhiweidada#qq.com，请把#换成@，本站将给予最大的支持与配合，做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明
本站所有资源不代表本站的观点或立场，基于网友分享，根据中国法律《信息网络传播权保护条例》第二十二条之规定，若资源存在侵权或相关问题请联系本站客服人员，zhiweidada#qq.com，请把#换成@，本站将给予最大的支持与配合，做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明