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GAN-Base-on-Matlab-master

于 2019-01-09 发布
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代码说明：

说明：  生成手写数字图片，一共提供了三种算法，example_1和example_2训练GAN来生成手写数字图片，搭建了一个简单的GAN的网络结构，example_3难度较大(There are three algorithms to generate handwritten digital pictures. Example_1 and example_2 train GAN to generate handwritten digital pictures, and build a simple network structure of GAN. Example_3 is more difficult.)

文件列表：

GAN-Base-on-Matlab-master, 0 , 2018-12-12
GAN-Base-on-Matlab-master\.gitignore, 5 , 2018-12-12
GAN-Base-on-Matlab-master\LICENSE, 1350 , 2018-12-12
GAN-Base-on-Matlab-master\README.md, 2762 , 2018-12-12
GAN-Base-on-Matlab-master\activation, 0 , 2018-12-12
GAN-Base-on-Matlab-master\activation\activate_z.m, 472 , 2018-12-12
GAN-Base-on-Matlab-master\activation\delta_activation_function.m, 559 , 2018-12-12
GAN-Base-on-Matlab-master\activation\delta_leaky_relu.m, 157 , 2018-12-12
GAN-Base-on-Matlab-master\activation\delta_relu.m, 144 , 2018-12-12
GAN-Base-on-Matlab-master\activation\delta_sigmoid.m, 213 , 2018-12-12
GAN-Base-on-Matlab-master\activation\delta_tanh.m, 67 , 2018-12-12
GAN-Base-on-Matlab-master\activation\leaky_relu.m, 169 , 2018-12-12
GAN-Base-on-Matlab-master\activation\relu.m, 83 , 2018-12-12
GAN-Base-on-Matlab-master\activation\sigmoid.m, 60 , 2018-12-12
GAN-Base-on-Matlab-master\error_term, 0 , 2018-12-12
GAN-Base-on-Matlab-master\error_term\delta_sigmoid_cross_entropy.m, 694 , 2018-12-12
GAN-Base-on-Matlab-master\error_term\get_error_term_from_atrous_conv2d_layer.m, 554 , 2018-12-12
GAN-Base-on-Matlab-master\error_term\get_error_term_from_batch_norm_layer.m, 1441 , 2018-12-12
GAN-Base-on-Matlab-master\error_term\get_error_term_from_conv2d_layer.m, 554 , 2018-12-12
GAN-Base-on-Matlab-master\error_term\get_error_term_from_conv2d_transpose_layer.m, 1936 , 2018-12-12
GAN-Base-on-Matlab-master\error_term\get_error_term_from_fully_connect_layer.m, 121 , 2018-12-12
GAN-Base-on-Matlab-master\error_term\get_error_term_from_reshape_layer.m, 137 , 2018-12-12
GAN-Base-on-Matlab-master\error_term\get_error_term_from_sub_sampling_layer.m, 374 , 2018-12-12
GAN-Base-on-Matlab-master\error_term\sigmoid_cross_entropy.m, 284 , 2018-12-12
GAN-Base-on-Matlab-master\example_1.m, 1582 , 2018-12-12
GAN-Base-on-Matlab-master\example_2.m, 1144 , 2018-12-12
GAN-Base-on-Matlab-master\example_3.m, 1277 , 2018-12-12
GAN-Base-on-Matlab-master\gan_train.m, 2922 , 2018-12-12
GAN-Base-on-Matlab-master\gradient, 0 , 2018-12-12
GAN-Base-on-Matlab-master\gradient\calculate_gradient_for_atrous_conv2d_layer.m, 773 , 2018-12-12
GAN-Base-on-Matlab-master\gradient\calculate_gradient_for_batch_norm_layer.m, 731 , 2018-12-12
GAN-Base-on-Matlab-master\gradient\calculate_gradient_for_conv2d_layer.m, 739 , 2018-12-12
GAN-Base-on-Matlab-master\gradient\calculate_gradient_for_conv2d_transpose_layer.m, 1260 , 2018-12-12
GAN-Base-on-Matlab-master\gradient\calculate_gradient_for_fully_connect_layer.m, 179 , 2018-12-12
GAN-Base-on-Matlab-master\layer, 0 , 2018-12-12
GAN-Base-on-Matlab-master\layer\atrous_conv2d.m, 893 , 2018-12-12
GAN-Base-on-Matlab-master\layer\batch_norm.m, 1074 , 2018-12-12
GAN-Base-on-Matlab-master\layer\check_layer_field_names.m, 579 , 2018-12-12
GAN-Base-on-Matlab-master\layer\conv2d.m, 1062 , 2018-12-12
GAN-Base-on-Matlab-master\layer\conv2d_transpose.m, 1933 , 2018-12-12
GAN-Base-on-Matlab-master\layer\reshape_operation.m, 300 , 2018-12-12
GAN-Base-on-Matlab-master\layer\setup_atrous_conv2d_layer.m, 1631 , 2018-12-12
GAN-Base-on-Matlab-master\layer\setup_batch_norm_layer.m, 751 , 2018-12-12
GAN-Base-on-Matlab-master\layer\setup_conv2d_layer.m, 1594 , 2018-12-12
GAN-Base-on-Matlab-master\layer\setup_conv2d_transpose_layer.m, 5976 , 2018-12-12
GAN-Base-on-Matlab-master\layer\setup_fully_connect_layer.m, 704 , 2018-12-12
GAN-Base-on-Matlab-master\layer\setup_reshape_layer.m, 454 , 2018-12-12
GAN-Base-on-Matlab-master\layer\setup_sub_sampling_layer.m, 389 , 2018-12-12
GAN-Base-on-Matlab-master\layer\sub_sample.m, 346 , 2018-12-12
GAN-Base-on-Matlab-master\nerual_network_flow, 0 , 2018-12-12
GAN-Base-on-Matlab-master\nerual_network_flow\nn_applygrads_adam.m, 1408 , 2018-12-12
GAN-Base-on-Matlab-master\nerual_network_flow\nn_applygrads_sgd.m, 602 , 2018-12-12
GAN-Base-on-Matlab-master\nerual_network_flow\nn_bp_d.m, 2904 , 2018-12-12
GAN-Base-on-Matlab-master\nerual_network_flow\nn_bp_g.m, 2981 , 2018-12-12
GAN-Base-on-Matlab-master\nerual_network_flow\nn_ff.m, 1633 , 2018-12-12
GAN-Base-on-Matlab-master\nerual_network_flow\nn_setup.m, 1157 , 2018-12-12
GAN-Base-on-Matlab-master\readme_images, 0 , 2018-12-12
GAN-Base-on-Matlab-master\readme_images\1.png, 3660 , 2018-12-12
GAN-Base-on-Matlab-master\readme_images\2.png, 3589 , 2018-12-12
GAN-Base-on-Matlab-master\readme_images\3.png, 4283 , 2018-12-12
GAN-Base-on-Matlab-master\setup_environment.m, 296 , 2018-12-12
GAN-Base-on-Matlab-master\test, 0 , 2018-12-12
GAN-Base-on-Matlab-master\test\convolution_process.m, 1821 , 2018-12-12
GAN-Base-on-Matlab-master\util, 0 , 2018-12-12
GAN-Base-on-Matlab-master\util\argparse.m, 259 , 2018-12-12
GAN-Base-on-Matlab-master\util\expand.m, 1958 , 2018-12-12
GAN-Base-on-Matlab-master\util\flipall.m, 77 , 2018-12-12
GAN-Base-on-Matlab-master\util\insert_zeros_into_array.m, 329 , 2018-12-12
GAN-Base-on-Matlab-master\util\padding_height_width_in_array.m, 476 , 2018-12-12
GAN-Base-on-Matlab-master\util\save_images.m, 623 , 2018-12-12

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    主要是对视网膜图像进行血管分割，效果还不错(Primarily on retinal vascular image segmentation, the effect was not bad)
    2021-04-26 21:18:45下载
    积分：1
  • stereo
    立体视觉代码。主要过程包括：harris角点检测，zncc最优策略匹配，扩散得到更多特征对应，计算disparity和对应颜色值。另外提供了两幅图片以及对应的特征匹配的结果和disparity图像。(Three-dimensional visual codes. The main process includes: harris corner detection, zncc optimal strategy for matching, the proliferation of more features of the corresponding calculation of disparity and the corresponding color value. Also provides a picture and two corresponding feature matching results and disparity images.)
    2020-06-29 14:40:02下载
    积分：1
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    文献，基于融合和聚类的遥感图像变化检测技术(Literature, based on remote sensing image fusion and change detection clustering technology)
    2013-12-09 22:53:30下载
    积分：1
  • Susan
    运用susan算法进行角点检测，并在图中显示出来(Application susan corner detection algorithm and displayed in the drawing)
    2016-03-14 17:17:52下载
    积分：1
  • xtx
    本代码是基于数学形态学的多尺度多结构算子的腐蚀与膨胀算法(This code is corrosion and expansion algorithm based on mathematical morphology of multi-scale multi-structure operator)
    2013-03-13 14:48:37下载
    积分：1
  • deedsMIND
    基于mind相似性度量的图像配准，配准精度很高。效果不错。(Mind based on similarity measure of image registration, the registration precision is high. The result is right.)
    2014-12-04 13:42:32下载
    积分：1
  • 1
    说明：  详细介绍了多普勒天气雷达系统建模与仿真应用 (Details of the Doppler Weather Radar System Modeling and Simulation Application)
    2010-08-22 10:19:49下载
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  • SDK采集图像（Halcon）
    需安装Halcon
    2020-02-21下载
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  • kuaisutisheng
    针对CT 医学图像和MR I 医学图像成像特点, 提出了基于快速整数提升小波变换的融合方法。在CT 和 MR I 两幅医学图像配准的前提下, 利用提升小波变换把图像分解成低频和高频子图像, 对于小波变换后的高频 子图像, 选择区域标准差大的作为融合后的子图像 对于低频子图像, 采用加权融合, 最后进行小波逆变换, 得到 融合后的图像, 并对融合后图像用信息熵、平均梯度、相关系数的指标进行评价。实验结果表明, 基于快速整数提 升小波变换融合中, 小波高低频系数采用不同的规则能够取得更好的融合效果, 其轮廓清晰。该算法能够提升融 合后的医学图像信息量, 同时有效地保护图像的细节信息, 在执行时间和图像质量上均优于普通小波算法。(A imed at the characterist ics of CT andMR Imedical images, a new image fu sion al2 go rithm based on the fast in t lif t ing w avelet t ran sfo rm s is p ropo sed. Two o riginal images are decompo sed by the fast in t lif t ing w avelet t ran sfo rm s. The fu sion ru le based on the max imum standard deviat ion value variance is u sed to fu se the h igh f requency sub2image, w h ile a w eigh t2 ed average fu sed ru le is app lied by coeff icien t s of the low f requency. F inally, the fu sion image is recon st ructed fo r perfo rm ing inverse fast in t lif t ing w avelet t ran sfo rm s. The fu sion image is evaluated by en t ropy, average gradien t, and co rrelat ion coeff icien t s. Experimen tal resu lt show s that the fu sion image hasmo re info rmat ion than o riginal images and imp roves the quali2 ty of o riginal images. M eanw h ile, the fu sion image p ro tect s detail characterist ics of the image, thu s the real2t ime p rocess and image qualit ies are w ell than tha)
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