文件列表:

config.py (1048, 2017-09-04)
download_imagenet.py (2169, 2017-09-04)
img (0, 2017-09-04)
img\SRGAN Result.pptx (11568038, 2017-09-04)
img\SRGAN Result2.pptx (13720555, 2017-09-04)
img\SRGAN Result3.pptx (687184, 2017-09-04)
img\SRGAN_Result.png (505606, 2017-09-04)
img\SRGAN_Result2.png (485861, 2017-09-04)
img\SRGAN_Result3.png (1234580, 2017-09-04)
img\model.jpeg (287980, 2017-09-04)
main.py (16671, 2017-09-04)
model.py (23290, 2017-09-04)
tensorlayer (0, 2017-09-04)
tensorlayer\__init__.py (677, 2017-09-04)
tensorlayer\activation.py (3284, 2017-09-04)
tensorlayer\cost.py (22817, 2017-09-04)
tensorlayer\db.py (16624, 2017-09-04)
tensorlayer\files.py (33051, 2017-09-04)
tensorlayer\iterate.py (15994, 2017-09-04)
tensorlayer\layers.py (264811, 2017-09-04)
tensorlayer\nlp.py (32835, 2017-09-04)
tensorlayer\ops.py (5981, 2017-09-04)
tensorlayer\prepro.py (62978, 2017-09-04)
tensorlayer\rein.py (3359, 2017-09-04)
tensorlayer\utils.py (22312, 2017-09-04)
tensorlayer\visualize.py (12432, 2017-09-04)
utils.py (819, 2017-09-04)

## Super Resolution Examples We run this script under [TensorFlow](https://www.tensorflow.org) 1.2 and the self-contained [TensorLayer](http://tensorlayer.readthedocs.io/en/latest/). If you got error, you may need to update TensorLayer. This repo may be merged into [tensorlayer](https://github.com/zsdonghao/tensorlayer) soon. ### SRGAN Architecture TensorFlow Implementation of ["Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"](https://arxiv.org/abs/1609.04802)

### Results

### Prepare Data and Pre-trained VGG - 1. You need to download the pretrained VGG19 model in [here](https://mega.nz/#!xZ8glS6J!MAnE91ND_WyfZ_8mvkuSa2YcA7q-1ehfSm-Q1fxOvvs) as [tutorial_vgg19.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_vgg19.py) show. - 2. You need to have the high resolution images for training. - In this experiment, I used images from [DIV2K - bicubic downscaling x4 competition](http://www.vision.ee.ethz.ch/ntire17/), so the hyper-paremeters in `config.py` (like number of epochs) are seleted basic on that dataset, if you change a larger dataset you can reduce the number of epochs. - If you dont want to use DIV2K dataset, you can also use [Yahoo MirFlickr25k](http://press.liacs.nl/mirflickr/mirdownload.html), just simply download it using `train_hr_imgs = tl.files.load_flickr25k_dataset(tag=None)` in `main.py`. - If you want to use your own images, you can set the path to your image folder via `config.TRAIN.hr_img_path` in `config.py`. ### Run - Set your image folder in `config.py`, if you download [DIV2K - bicubic downscaling x4 competition](http://www.vision.ee.ethz.ch/ntire17/) dataset, you don't need to change it. - Other links for DIV2K, in case you can't find it : [test\_LR\_bicubic_X4](https://data.vision.ee.ethz.ch/cvl/DIV2K/validation_release/DIV2K_test_LR_bicubic_X4.zip), [train_HR](https://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip), [train\_LR\_bicubic_X4](https://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_LR_bicubic_X4.zip), [valid_HR](https://data.vision.ee.ethz.ch/cvl/DIV2K/validation_release/DIV2K_valid_HR.zip), [valid\_LR\_bicubic_X4](https://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_valid_LR_bicubic_X4.zip). ```python config.TRAIN.img_path = "your_image_folder/" ``` - Start training. ```bash python main.py ``` - Start evaluation. ```bash python main.py --mode=evaluate ``` ### Reference * [1] [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://arxiv.org/abs/1609.04802) * [2] [Is the deconvolution layer the same as a convolutional layer ?](https://arxiv.org/abs/1609.07009) ### License - For academic and non-commercial use only. - For commercial use, please contact dhsig552@163.com.

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