搜索资源列表
SRCNN
- 基于深度学习的图像超分辨率算法。 参考论文 Learning a Deep Convolutional Network for Image Super-Resolution (ECCV 2014) -Super resolution based on deep learning Refer to Learning a Deep Convolutional Network for Image Super-Resolution (ECCV 2014)
Test-code-for-SRCNN
- 深度学习，超分辨卷积神经网络，获取测试数据的MATLAB代码-Depth study, the super-resolution convolution neural network, access to test data MATLAB code
SRCNN
- 深度卷积神经网络超分辨，来自于ECCV2014一篇文章的代码，代码好用-Depth of the convolutional neural network super resolution, the ECCV2014 an article of the code, code easy to use
SRCNN
- 通过卷积神经网络CNN实现超分辨率重建，利用训练模型实现参数和权重偏移的训练，达到输入低分辨率图像，输出高分辨率图像，测试例程附录。-Convolution neural network CNN to achieve super-resolution reconstruction using a training model parameters and to achieve weight shift training to achieve a low-resolution image inpu
69491709Training-code-for-SRCNN
- 先用训练样本对模型SRCNN进行训练，然后使用测试样本测试模型的有效性-First with the effectiveness of the model SRCNN training samples for training, and then use the test sample test model
Test code for SRCNN
- SRCNN代码实现。该代码使用三层卷积神经网络，进行图像的超分辨率重建，效果比双三次插值好很多(The code uses three layer convolutional neural network for image super-resolution reconstruction, the effect is much better than the double three interpolation.)
SRCNN_train
- 基于深度学习的超分辨率重建，三层卷积神经网络(super resolutino deep learning)
SRCNN-master
- 提升图像再放大后的清晰度，代码中已有网络模型，需要进行训练(The sharpness of the enhanced image, the network model in the code, which needs to be trained)
SRCNN_TEST
- 基于卷积神经网络的图像超分辨率重建，不含训练程序，包含已训练好的model !(Test code for Super-Resolution Convolutional Neural Networks (SRCNN))
SRCNN train
- 利用matlab做深度学习，SRCNN作为超分辨率重建开山之作，用matlab进行代码复现(Matlab to do deep learning, SRCNN as a super resolution reconstruction work, using matlab for code reproduction)