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首页 » Python » 基于深度卷积神经网络图像去噪算法

基于深度卷积神经网络图像去噪算法

于 2020-10-15 发布
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下载积分: 1 下载次数: 6
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代码说明：

说明：  用于图像去噪处理，使用ADM方法图像去噪处理器处理(Used for image denoising processing, using adm method image denoising processor processing)

文件列表：

DnCNN-Denoise-Gaussian-noise-TensorFlow-master, 0 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\DnCNN.py, 2950 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES, 0 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\denoised1.jpg, 9243 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\denoised2.jpg, 6945 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\denoised3.jpg, 9026 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\denoised4.jpg, 11065 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\denoised5.jpg, 11102 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\denoised6.jpg, 9139 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\denoised7.jpg, 10044 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\method.jpg, 39256 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\noised1.jpg, 20627 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\noised2.jpg, 17248 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\noised3.jpg, 18682 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\noised4.jpg, 20420 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\noised5.jpg, 19110 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\noised6.jpg, 20174 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\noised7.jpg, 21734 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\LICENSE, 1067 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\README.md, 3367 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet, 0 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\01.png, 38267 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\02.png, 34985 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\03.png, 40181 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\04.png, 42947 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\05.png, 40728 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\06.png, 40985 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\07.png, 39804 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\08.png, 151065 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\09.png, 185727 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\10.png, 177762 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\11.png, 209817 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\12.png, 193637 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingResults, 0 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingResults\0_1440.jpg, 1847 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingResults\0_1520.jpg, 1830 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingResults\0_1600.jpg, 2277 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingSet, 0 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingSet\1_17.jpg, 674 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingSet\1_18.jpg, 619 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingSet\1_19.jpg, 648 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingSet\1_20.jpg, 579 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingSet\1_25.jpg, 665 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingSet\1_26.jpg, 677 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingSet\1_27.jpg, 640 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingSet\1_28.jpg, 611 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\config.py, 106 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\network.py, 557 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\ops.py, 4376 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\save_para, 0 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\save_para\READMEN.txt, 30 , 2019-03-02

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0 个回复

  • wode1
    是一个很好的图像拼接程序，先用sift算法提取两幅图片的特征点再用算法筛去错配点，把匹配点用线连接起来，注意：主函数是match.m，运行主函数后在命令框输入运行match( image1.jpg , image2.jpg )(Is a good image stitching program, the first two pictures with the sift algorithm feature extraction algorithm and then weed out the wrong point with point, the match point with a line linking Note: The main function is match.m, after running the main function Enter the run command box match (' image1.jpg' , ' image2.jpg' ))
    2011-10-17 16:07:23下载
    积分：1
  • frft_candon
    离散分数阶傅立叶变换，算法参考 C. Candan, M.A. Kutay, and H.M. Ozaktas. The discrete Fractional Fourier Transform. IEEE Trans. Sig. Proc., 48:1329--1337, 2000 (Discrete fractional Fourier transform algorithm reference C. Candan, MA Kutay, and HM Ozaktas. The discrete Fractional Fourier Transform. IEEE Trans. Sig. Proc., 48:1329- 1337, 2000)
    2013-10-23 18:27:29下载
    积分：1
  • celledge7
    用MATLAB实现的细胞边缘检测,癌细胞颜色分析,癌细胞形态学分析(MATLAB cell edge detection, color cell analysis, morphological analysis of cancer)
    2006-11-16 16:58:42下载
    积分：1
  • C# 在线生成各种条码ean13,129,code39
    【实例介绍】
    2015-01-27下载
    积分：1
  • Segmentation
    使用区域生长法对灰度图像进行分割，显示原图及分割后的图像(Region growing method using gray-scale image segmentation, image display artwork and divided)
    2013-11-26 14:17:07下载
    积分：1
  • plot_brains
    通过对于CT图像的读取来进行三维的脑部显示，有助于学习基于matlab的CT图像三维重建过程(Conducive to learning through the reading of CT images to three- dimensional brain display matlab- based CT image 3D reconstruction process )
    2012-07-13 18:16:43下载
    积分：1
  • GMMsegmation
    利用高斯混合模型进行视频前景运动目标的提取！请使用自己的AVI文件。(extract the foreground moving object using the Gaussian mixture model)
    2011-07-04 11:24:16下载
    积分：1
  • ICBI
    一种基于双立方插值的新型图像插值算法，较好的保存边缘信息(Based on bicubic new image interpolation algorithm, the preservation of edge information better)
    2021-03-29 15:59:11下载
    积分：1
  • bianyuanjiance
    说明：  多种边缘检测算子对比 不需要改动 有sobel prewittts canny log(Multiple edge detection operators)
    2020-06-18 20:55:08下载
    积分：1
  • train-images-idx3-ubyte
    MNIST数据集中图像数据文件， 60000个训练集(The MNIST dataset image data files, 60000 training set)
    2012-10-11 10:56:28下载
    积分：1
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