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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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    说明：  在计算机视觉中，图像分割是将图像分成多个片段的过程。这使我们能够通过以比原始图像更简单的表示方式来更好地分析图像。一种用于分割图像的方法是阈值分割；也就是说，将强度值低于阈值的所有像素设置为背景值，而将高于阈值的其他像素设置为前景值。最简单的阈值处理形式是对所有像素使用全局阈值，而自适应阈值处理根据像素及其周围环境动态改变阈值。典型地，阈值处理从灰度图像开始，并输出二进制图像以清晰地描绘图像中的片段。在本实验中，我们开发了一种自适应阈值算法，并将其与更简单的算法进行了比较。 此外，我们还开发了逆运动学证明来将机械臂移动到给定的x，y，z坐标。这一证明涉及三种命名身份，并获得了对机械臂设计的更好理解。(Image threshold segmentation)
    2019-04-14 15:41:02下载
    积分：1
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    说明：  能将256色图像转换为黑白图像，程序简单易行，适合初学VC者练笔。(Able to convert 256-color image black and white images, the program is simple, suitable for beginners who Lianbi VC.)
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    积分：1
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    本程序能够实现图像处理中的图像细化，是基于matlab的。(This procedure can realize image processing image thinning is based on the matlab.)
    2008-03-16 11:01:19下载
    积分：1
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    无源双基地雷达直达波脉冲丢失与相位突变影响分析Passive bistatic radar direct wave pulse loss and phase change impact analysis(Passive bistatic radar direct wave pulse loss and phase change impact analysis)
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    2007-05-26 08:08:04下载
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    2016-04-24 04:49:00下载
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    2012-09-20 16:47:35下载
    积分：1
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