登录
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
0 322
下载积分: 1 下载次数: 6
我要下载

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

下载说明：请别用迅雷下载，失败请重下，重下不扣分！

发表评论

0 个回复

  • segmentation
    运用图聚类实现SAR图像的分割，效果明显，程序简洁。(the segmentation of SAR)
    2010-06-06 14:43:09下载
    积分：1
  • Wavelet
    Visual C++下的小波变换，小波分解与重构，加入噪声分析，显示分解和重构图形。(Wavelets for data encode and uncode, the figures would be shown.)
    2009-04-09 21:55:21下载
    积分：1
  • duochidushang
    说明：  提出了一种基于数学形态学的多尺度熵权边缘检测方法。信息熵为图像的内在特性，由它来决定不同结构元 素所提取的边缘在最终合成边缘中所占的比重，是自适应的和客观的(A multi-scale based on mathematical morphology edge detection method of entropy. Information entropy inherent characteristics of the image, from which different structural elements to determine the edge of the extracted edge in the final synthesis of proportion, is adaptive and objective)
    2010-04-19 18:19:25下载
    积分：1
  • h264
    说明：  通过websocket接收h264裸流后，在web端解析播放(After receiving the bare stream of h264 through websocket, it is parsed and played on the web side)
    2021-03-07 10:09:29下载
    积分：1
  • 精通Matlab数字图像处理与识别
    说明：  数字图像处理(Digital Image Processing)是通过计算机对图像进行去除噪声、增强、复原、分割、提取特征等处理的方法和技术。 [1] 数字图像处理的产生和迅速发展主要受三个因素的影响：一是计算机的发展；二是数学的发展（特别是离散数学理论的创立和完善）;三是广泛的农牧业、林业、环境、军事、工业和医学等方面的应用需求的增长(Digital Image Processing (DIP) is a method and technology of removing noise, enhancing, restoring, segmenting and extracting features from images by computer. [1] The emergence and rapid development of digital image processing are mainly influenced by three factors: first, the development of computer; second, the development of mathematics (especially the establishment and improvement of discrete mathematics theory); third, the growth of the demand for extensive applications in agriculture, animal husbandry, forestry, environment, military, industry and medicine.)
    2020-06-23 22:20:02下载
    积分：1
  • ghost-imaging
    利用双光子纠缠方法进行双路轨道角动量光学鬼成像过程。(Using two-photon entanglement the dual orbital angular momentum of optical ghost imaging process.)
    2021-02-25 00:19:39下载
    积分：1
  • hingfun
    非常适合计算机视觉方面的研究使用,包括四元数的各种计算,算法优化非常好，几乎没有循环。( Very suitable for the study using computer vision, Including quaternion various calculations, Algorithm optimization is very good, almost no circulation.)
    2017-02-07 16:03:47下载
    积分：1
  • GaussianPyramid
    对灰度图像建立高斯金字塔，可以调整核的参数。(Establish Gaussian pyramid for gray-scale images to , you can adjust the reduce parameters.)
    2009-10-10 15:50:32下载
    积分：1
  • Matlab_source_for_edge_detector_using_quaternion_c
    "基于四元数卷积的彩色图像边缘检测"一文的matlab源代码，很有帮助(matlab source code of "Colour image edge detector based on quaternion convolution" Electronics Letters, 34(10), May 14 1998, 969-971)
    2010-01-26 11:56:25下载
    积分：1
  • 2
    说明：  采用haar小波实现二维图像的分解与重构（mallat算法）：(Two-dimensional image using haar wavelet decomposition and reconstruction (mallat algorithm):)
    2013-07-16 11:38:37下载
    积分：1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载

热门标签

Visual C++ matlab C/C++ Java Others Unix_Linux EasyLanguage易语言 Visual Basic C# PDF C++ Delphi VHDL ASP WINDOWS
热门下载

Copyright© 2019-2026 | Powered by Imdn | 浙ICP备16007336号 | IMDN交流 | 联系客服（QQ：3324461878）

本网站资源收集自网络及网站用户上传，若有侵权请邮件至（3324461878@qq.com），核实后我们会在24小时内删除，谢谢！