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Superresolution

example/gluon/super_resolution/README.md

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Superresolution

Note: this example use The BSDS500 Dataset which is copyright Berkeley Computer Vision Group. For more details, see dataset website

This example trains a convolutional neural network to enhance the resolution of images (also known as superresolution). The script takes the following commandline arguments:

Super-resolution using an efficient sub-pixel convolution neural network.

optional arguments:
  -h, --help            show this help message and exit
  --upscale_factor UPSCALE_FACTOR
                        super resolution upscale factor. default is 3.
  --batch_size BATCH_SIZE
                        training batch size, per device. default is 4.
  --test_batch_size TEST_BATCH_SIZE
                        test batch size
  --epochs EPOCHS       number of training epochs
  --lr LR               learning Rate. default is 0.001.
  --use-gpu             whether to use GPU.
  --seed SEED           random seed to use. Default=123
  --resolve_img RESOLVE_IMG
                        input image to use

Once the network is trained you can use the following command to increase the resolution of your image:

python  super_resolution.py --resolve_img myimage.jpg

Citation

<b>Contour Detection and Hierarchical Image Segmentation P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. IEEE TPAMI, Vol. 33, No. 5, pp. 898-916, May 2011. PDF BibTex </b>