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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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  • MRF
    Markov随机场的例子程序，包括ICM，BP算法，matlab编写，共30几个函数。(Markov random field examples of procedures, including ICM, BP algorithm, matlab prepared a total of 30 number of functions.)
    2009-03-04 21:50:56下载
    积分：1
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    2020-06-19 18:31:45下载
    积分：1
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    说明：  利用特征提取和分类的方法,假设输入值为{大,圆,光滑}为水果的三个特征值,来代表三种不同的水果,识别苹果\梨\香蕉\桔子\菠萝水果类别. (The use of feature extraction and classification methods, assuming that the import value was (large, round, smooth fruit) for the three characteristics of value, to represent three different types of fruit, to identify Apple \ pear \ banana \ orange \ pineapple fruit category.)
    2021-03-16 11:19:22下载
    积分：1
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    说明：  主要颜色谱 计算主要的颜色来舍弃其他的颜色，用少数的颜色个数代替大多数进行聚类(Main color spectrum calculation primary colors to abandon other colors)
    2013-03-12 14:18:09下载
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  • Pseudo-Color Image Processing
    数字图像处理实验-图像处理-含实验题目及源代码(Pseudo-Color Image Processing (a) Implement Fig. 6.23, with the characteristic that you can specify two ranges of gray-level values for the input image and your program will output an RGB image whose pixels have a specified color corresponding to one range of gray levels in the input image, and the remaining pixels in the RGB image have the same shade of gray as they had in the input image. You can limit the input colors to all the colors in Fig. 6.4(a). (b) Download the image in Fig. 1.10(4) and process it with your program so that the river appears yellow and the rest of the pixels are the same shades of gray as in the input image. It is acceptable to have isolated specs in the image that also appear yellow, but these should be kept as few as possible by proper choice of the two gray-level bands that you input into your program.)
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    2019-07-03 10:34:56下载
    积分：1
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    Image processing of band reject filter and archive particular bit of an 8 bit pixel of an image
    2013-09-28 18:40:35下载
    积分：1
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    斯坦福大学的姚邦鹏开发的一个图像分类算法，使用random forest实现了图像的精细区域描述，赢得了PASCAL VOC2011图像分类竞赛中的winner prize.经测试，程序完全可运行，且提供了MAC和Windows下的两种程序。(Image Classification: An Integration of Randomization and Discrimination in A Dense Feature Representation The goal of our method is to identify the discriminative fine-grained image region that distinguishes different classes. To achieve this goal we sample image regions from dense sampling space and use a random forest algorithm with discriminative classifier. Each node of the tree of random forest is trained and tested with fine-grained image patches combining the information from upstream nodes together. We implemented each node of the tree with a discriminative SVM classifier, which makes the node as a strong classifier. )
    2012-10-30 16:17:30下载
    积分：1
  • saomiaoxian
    这是一个扫描线种子填充算法的程序，这个程序的演示效果不错(This is a scan line seed fill algorithm program, this program demonstrates good results)
    2020-06-30 20:40:02下载
    积分：1
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