-
Notifications
You must be signed in to change notification settings - Fork 30
Home
Welcome to the dl-image-enhance wiki!
dl-image-enhance project implements algorithms described in [1].
It should be used along with another project cuda_convnet_plus to reproduce the experimental results reported in [1].
- Adapt ${DL_IMAGE_ENHANCE_ROOT}/setup.sh.example. Set environment variable PROJ_DIR properly
- Rename it to setup.sh. Run the script ". setup.sh" to set up the environment.
There are three local effect datasets, namely Foreground Pop-out, Local Xpro and Watercolor.
Original images path : dl-image-enhance/data/uniform_set/uniform_set_autotone_tif
Foreground Pop-out images path: data/uniform_set_foregroundpopout/uniform_set_foregroundpopout_tif
Local Xpro images path: data/uniform_set_xpro/uniform_set_xpro_tif
Watercolor images path: data/uniform_set_watercolor/uniform_set_watercolor_tif
We prepare scripts to reproduce the results of Foreground Pop-out effect in the paper [1]. Please sequentially execute the following scripts.
- Prepare training data batches for neural network training.
Script: ${DL_IMAGE_ENHANCE_ROOT}/py/script_prepare_train_batches_uniform_set_foregroundpopout.sh
- Optionally, train a deep net. Otherwise, use the a provided trained model to enhance image at testing stage.
Script: ${CUDA_CONVNET_PLUS_ROOT}/py/script_train_uniform_set_foregroundpopout.sh
- Use a pre-trained neural network to enhance testing images. The unit of enhancement is super-pixel. The enhanced testing images will be saved in training checkpoint summary folder ${DL_IMAGE_ENHANCE_ROOT}/data/uniform_set_foregroundpopout/convnet_checkpoints/ConvNet__2015-01-06_20.11.39zyan3-server2_summary/superpixel
Script: ${DL_IMAGE_ENHANCE_ROOT}/py/script_enhance_images_uniform_set_foregroundpopout.sh
Please sequentially execute the following scripts to reproduce results of Local Xpro effect.
- ${DL_IMAGE_ENHANCE_ROOT}/py/script_prepare_train_batches_uniform_set_xpro.sh
- ${CUDA_CONVNET_PLUS_ROOT}/py/script_train_uniform_set_pro.sh
- ${DL_IMAGE_ENHANCE_ROOT}/py/script_enhance_images_uniform_set_xpro.sh.
The enhanced testing images will be saved in training checkpoint summary folder ${DL_IMAGE_ENHANCE_ROOT}/data/uniform_set_xpro/convnet_checkpoints/ConvNet__2015-01-06_14.45.07zyan3-server2_summary/superpixel
Please sequentially execute the following scripts to reproduce results of Watercolor effect.
- ${DL_IMAGE_ENHANCE_ROOT}/py/script_prepare_train_batches_uniform_set_watercolor.sh
- ${CUDA_CONVNET_PLUS_ROOT}/py/script_train_uniform_set_watercolor.sh
- ${DL_IMAGE_ENHANCE_ROOT}/py/script_enhance_images_uniform_set_watercolor.sh.
The enhanced testing images will be saved in training checkpoint summary folder ${DL_IMAGE_ENHANCE_ROOT}/data/uniform_set_watercolor/convnet_checkpoints/ConvNet__2015-01-06_14.45.52zyan3-server2_summary/superpixel
- Zhicheng Yan, Hao Zhang, Baoyuan Wang, Sylvain Paris, Yizhou Yu. Automatic Photo Adjustment Using Deep Neural Networks. ACM Transactions on Graphics (TOG), 2015