资源算法-TFCode-SuperResolution-SRDenseNet

-TFCode-SuperResolution-SRDenseNet

2020-02-04 | |  73 |   0 |   0

[Super Resolution] SRDenseNet - tensorflow implementation

tensorflow implementation of SRDenseNet

Prerequisites

  • python 3.x

  • Tensorflow > 1.x

  • Pillow

  • OpenCV

  • argparse

Properties (what's different from reference code)

  • This code requires Tensorflow. This code was fully implemented based on Python 3 differently from the original.

  • This code supports both RGB and YCBCR channel space.

  • This code supports tensorboard summarization

  • This code supports model saving and restoration

  • This code uses PILLOW library to resize image. Note that the performance of Bicubic function in PILLOW is lower than that of Matlab library.

  • Inference code should be changed to suit your environment.

  • This code is suitable for practical usage rather than research.

  • We use Resize-convolution instead Transpose-convolution. It is more flexible to handle various scale factor.

Usage

usage: python3 trainer.py --gpu 0 

[options]
""" system """
parser.add_argument("--exp_type", type=int, default=0, help='experiment type')
parser.add_argument("--gpu", type=str, default=1)  # -1 for CPU
parser.add_argument("--model_tag", type=str, default="default", help='Exp name to save logs/checkpoints.')
parser.add_argument("--checkpoint_dir", type=str, default='../__outputs/checkpoints/', help='Dir for checkpoints.')
parser.add_argument("--summary_dir", type=str, default='../__outputs/summaries/', help='Dir for tensorboard logs.')
parser.add_argument("--restore_model_file", type=str, default=None, help='file for restoration')
#parser.add_argument("--restore_model_file", type=str, default='../__outputs/checkpoints/SRDenseNet_SRDenseNet_model_default_10_11_00_55_14/model.ckpt-47000', help='file for resotration')
""" model """
parser.add_argument("--batch_size", type=int, default=16, help='Minibatch size(global)')
parser.add_argument("--patch_size", type=int, default=54, help='Minipatch size(global)')
#parser.add_argument("--patch_stride", type=int, default=13, help='patch stride') #we just sample patches randomly for simplicity
parser.add_argument("--operating_channel", type=str, default="RGB", help="operating channel [RGB, YCBCR")  # -1 for CPU
parser.add_argument("--num_channels", type=int, default=3, help='the number of channels')
parser.add_argument("--scale", type=int, default=3, help='scaling factor')
parser.add_argument("--data_root_train", type=str, default="./dataset/SR_training_datasets/T91", help='Data root dir')
parser.add_argument("--data_root_test", type=str, default="./dataset/SR_testing_datasets/Set5", help='Data root dir')
  • For running tensorboard, tensorboard --logdir=../__outputs/summaries then access localhost:6006 with your browser

Result [Bicubic / SRDenseNet / Label (x3)]

SRDense_1.png

图片.png

References

Author

Dohyun Kim


上一篇:SR_SRDenseNet_tensorflow

下一篇:unsupervised-depthnet

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