testing_code
所属分类：网络编程
开发工具：matlab
文件大小：4791KB
下载次数：8
上传日期：2020-07-14 11:31:42
上 传 者：
hero!
说明： 将训练之后的模型进行测试，对单幅图像进行相应的去雨
(Removing rain from single images via a deep detail network)
文件列表:
code (0, 2017-03-01)
code\demo.m (1627, 2017-03-01)
code\fast-guided-filter-code-v1 (0, 2017-03-01)
code\fast-guided-filter-code-v1\boxfilter.m (931, 2010-07-08)
code\fast-guided-filter-code-v1\fastguidedfilter.m (1300, 2015-05-05)
code\fast-guided-filter-code-v1\fastguidedfilter_color.m (3092, 2015-05-05)
code\image (0, 2017-03-01)
code\image\real_world (0, 2017-03-01)
code\image\real_world\1.jpg (460050, 2016-10-11)
code\image\real_world\2.jpg (525601, 2016-10-11)
code\image\real_world\3.jpg (233751, 2016-10-11)
code\image\synthetic (0, 2017-03-01)
code\image\synthetic\1ground_truth.jpg (174448, 2016-01-12)
code\image\synthetic\1rain.jpg (77884, 2016-04-15)
code\image\synthetic\2ground_truth.jpg (204181, 2016-01-14)
code\image\synthetic\2rain.jpg (60804, 2016-01-14)
code\image\synthetic\3ground_truth.jpg (373692, 2016-01-14)
code\image\synthetic\3rain.jpg (76725, 2016-01-14)
code\matconvnet (0, 2017-03-01)
code\matconvnet\COPYING (735, 2016-09-04)
code\matconvnet\Makefile (8623, 2016-09-04)
code\matconvnet\Makefile.mex (793, 2016-09-04)
code\matconvnet\Makefile.nvcc (925, 2016-09-04)
code\matconvnet\doc (0, 2017-03-01)
code\matconvnet\doc\Makefile (2071, 2016-09-04)
code\matconvnet\doc\blocks.tex (20209, 2016-09-04)
code\matconvnet\doc\figures (0, 2017-03-01)
code\matconvnet\doc\figures\imnet.pdf (18884, 2016-09-04)
code\matconvnet\doc\figures\pepper.pdf (702358, 2016-09-04)
code\matconvnet\doc\figures\svg (0, 2017-03-01)
code\matconvnet\doc\figures\svg\conv.svg (68592, 2016-09-04)
code\matconvnet\doc\figures\svg\convt.svg (65347, 2016-09-04)
code\matconvnet\doc\fundamentals.tex (21296, 2016-09-04)
code\matconvnet\doc\geometry.tex (16543, 2016-09-04)
code\matconvnet\doc\impl.tex (16698, 2016-09-04)
... ...
# MatConvNet: CNNs for MATLAB
**MatConvNet** is a MATLAB toolbox implementing *Convolutional Neural
Networks* (CNNs) for computer vision applications. It is simple,
efficient, and can run and learn state-of-the-art CNNs. Several
example CNNs are included to classify and encode images. Please visit
the [homepage](http://www.vlfeat.org/matconvnet) to know more.
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