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SRCNN-Tensorflow

于 2020-11-28 发布 文件大小:40540KB
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下载积分: 1 下载次数: 29
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代码说明：

  SRCNN Superresolution imteplated by tensorflow SRCNN tensorflow 实现(SRCNN Superresolution imteplated by tensorflow)

文件列表：

SRCNN-Tensorflow
SRCNN-Tensorflow\.git
SRCNN-Tensorflow\.gitignore
SRCNN-Tensorflow\.git\branches
SRCNN-Tensorflow\.git\config
SRCNN-Tensorflow\.git\description
SRCNN-Tensorflow\.git\HEAD
SRCNN-Tensorflow\.git\hooks
SRCNN-Tensorflow\.git\hooks\applypatch-msg.sample
SRCNN-Tensorflow\.git\hooks\commit-msg.sample
SRCNN-Tensorflow\.git\hooks\post-update.sample
SRCNN-Tensorflow\.git\hooks\pre-applypatch.sample
SRCNN-Tensorflow\.git\hooks\pre-commit.sample
SRCNN-Tensorflow\.git\hooks\pre-push.sample
SRCNN-Tensorflow\.git\hooks\pre-rebase.sample
SRCNN-Tensorflow\.git\hooks\prepare-commit-msg.sample
SRCNN-Tensorflow\.git\hooks\update.sample
SRCNN-Tensorflow\.git\index
SRCNN-Tensorflow\.git\info
SRCNN-Tensorflow\.git\info\exclude
SRCNN-Tensorflow\.git\logs
SRCNN-Tensorflow\.git\logs\HEAD
SRCNN-Tensorflow\.git\logs\refs
SRCNN-Tensorflow\.git\logs\refs\heads
SRCNN-Tensorflow\.git\logs\refs\heads\master
SRCNN-Tensorflow\.git\logs\refs\remotes
SRCNN-Tensorflow\.git\logs\refs\remotes\origin
SRCNN-Tensorflow\.git\logs\refs\remotes\origin\HEAD
SRCNN-Tensorflow\.git\objects
SRCNN-Tensorflow\.git\objects\info
SRCNN-Tensorflow\.git\objects\pack
SRCNN-Tensorflow\.git\objects\pack\pack-dbd6a604e1866e91edabf7286692f0cef22a1343.idx
SRCNN-Tensorflow\.git\objects\pack\pack-dbd6a604e1866e91edabf7286692f0cef22a1343.pack
SRCNN-Tensorflow\.git\packed-refs
SRCNN-Tensorflow\.git\refs
SRCNN-Tensorflow\.git\refs\heads
SRCNN-Tensorflow\.git\refs\heads\master
SRCNN-Tensorflow\.git\refs\remotes
SRCNN-Tensorflow\.git\refs\remotes\origin
SRCNN-Tensorflow\.git\refs\remotes\origin\HEAD
SRCNN-Tensorflow\.git\refs\tags
SRCNN-Tensorflow\checkpoint
SRCNN-Tensorflow\LICENSE
SRCNN-Tensorflow\main.py
SRCNN-Tensorflow\model.py
SRCNN-Tensorflow\README.md
SRCNN-Tensorflow\result
SRCNN-Tensorflow\result\bicubic.png
SRCNN-Tensorflow\result\orig.png
SRCNN-Tensorflow\result\srcnn.png
SRCNN-Tensorflow\sample
SRCNN-Tensorflow\Test
SRCNN-Tensorflow\Test\Set14
SRCNN-Tensorflow\Test\Set14\baboon.bmp
SRCNN-Tensorflow\Test\Set14\barbara.bmp
SRCNN-Tensorflow\Test\Set14\bridge.bmp
SRCNN-Tensorflow\Test\Set14\coastguard.bmp
SRCNN-Tensorflow\Test\Set14\comic.bmp
SRCNN-Tensorflow\Test\Set14\face.bmp
SRCNN-Tensorflow\Test\Set14\flowers.bmp
SRCNN-Tensorflow\Test\Set14\foreman.bmp
SRCNN-Tensorflow\Test\Set14\lenna.bmp
SRCNN-Tensorflow\Test\Set14\man.bmp
SRCNN-Tensorflow\Test\Set14\monarch.bmp
SRCNN-Tensorflow\Test\Set14\pepper.bmp
SRCNN-Tensorflow\Test\Set14\ppt3.bmp
SRCNN-Tensorflow\Test\Set14\zebra.bmp
SRCNN-Tensorflow\Test\Set5
SRCNN-Tensorflow\Test\Set5\baby_GT.bmp
SRCNN-Tensorflow\Test\Set5\bird_GT.bmp
SRCNN-Tensorflow\Test\Set5\butterfly_GT.bmp
SRCNN-Tensorflow\Test\Set5\head_GT.bmp
SRCNN-Tensorflow\Test\Set5\woman_GT.bmp
SRCNN-Tensorflow\Train
SRCNN-Tensorflow\Train\t1.bmp
SRCNN-Tensorflow\Train\t10.bmp
SRCNN-Tensorflow\Train\t11.bmp
SRCNN-Tensorflow\Train\t12.bmp
SRCNN-Tensorflow\Train\t13.bmp
SRCNN-Tensorflow\Train\t14.bmp
SRCNN-Tensorflow\Train\t15.bmp
SRCNN-Tensorflow\Train\t16.bmp
SRCNN-Tensorflow\Train\t17.bmp
SRCNN-Tensorflow\Train\t18.bmp
SRCNN-Tensorflow\Train\t19.bmp
SRCNN-Tensorflow\Train\t2.bmp
SRCNN-Tensorflow\Train\t20.bmp
SRCNN-Tensorflow\Train\t21.bmp
SRCNN-Tensorflow\Train\t22.bmp
SRCNN-Tensorflow\Train\t23.bmp
SRCNN-Tensorflow\Train\t24.bmp
SRCNN-Tensorflow\Train\t25.bmp
SRCNN-Tensorflow\Train\t26.bmp
SRCNN-Tensorflow\Train\t27.bmp
SRCNN-Tensorflow\Train\t28.bmp
SRCNN-Tensorflow\Train\t29.bmp
SRCNN-Tensorflow\Train\t3.bmp
SRCNN-Tensorflow\Train\t30.bmp
SRCNN-Tensorflow\Train\t31.bmp
SRCNN-Tensorflow\Train\t32.bmp

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    合成孔径雷达目标的特征提取及其识别技术。国防科大写的！(synthetic aperture radar target feature extraction and recognition technology. Written by which this university!)
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    卡尔曼滤波是一种数据处理方法，它是一种线性最小方差无偏估计准则，基于系统 状态估计和当前观测，通过引入状态空间而获得的新的状态估计.本篇论文陈述了卡尔曼滤 波的基本思路和算法；并通过仿真，显示卡尔曼滤波的功能，以及如何用它来跟踪方向确定、速度恒定的飞行器。 (Kalman filter is a data processing method, which is a linear minimum variance unbiased estimation criteria, based on system state estimation and the current observations, obtained by introducing the state space of the new state estimate of the Kalman filter thesis statement the basic ideas and algorithms and through simulation, shows Kalman filtering functions, and how to use it to keep track of the direction determined constant speed aircraft.)
    2013-09-06 10:21:27下载
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