Tensorflow implementation for learning an image-to-image color enhancement using CycleGAN structure (unsupervised).
For image example:
It learns color affine transform function for each pixel in CIE L*a*b*.
Network structure for transformation network looks:
This implementation is based on CycleGAN-tensorflow of xhujoy (https://github.com/xhujoy). This repository contains train and test codes for reproduce. Pretrained network model and dataset will be distributed soon.
- tensorflow r1.0 or higher version
- numpy 1.11.0
- scipy 0.17.0
- pillow 3.3.0
- Install tensorflow from https://github.com/tensorflow/tensorflow
- Clone this repo:
git clone https://github.com/JunhoJeon/unsupervised-color-enhance
cd CycleGAN-tensorflow- main.py: Main training/testing code
- model.py: CycleGAN model code for training and testing
- module.py: Defining network structure (affine transformation network)
- train.sh: Training shell script for parameterized training
To train a model,
CUDA_VISIBLE_DEVICES=0 python main.py --dataset_dir=/path/to/data/Models are saved to ./checkpoints/ (can be changed by passing --checkpoint_dir=your_dir).
To test the model,
CUDA_VISIBLE_DEVICES=0 python main.py --dataset_dir=/path/to/data/ --phase=test --which_direction=AtoB/BtoA- The tensorflow implementation of CyelcGAN (which this repository forked from), https://github.com/xhujoy/CycleGAN-tensorflow
- The torch implementation of CycleGAN, https://github.com/junyanz/CycleGAN
- The tensorflow implementation of pix2pix, https://github.com/yenchenlin/pix2pix-tensorflow
This software is being made available under the terms in the LICENSE file.
Any exemptions to these terms requires a license from the Pohang University of Science and Technology.
Project ‘COUPE’ aims to develop software that evaluates and improves the quality of images and videos based on big visual data. To achieve the goal, we extract sharpness, color, composition features from images and develop technologies for restoring and improving by using it. In addition, personalization technology through user preference analysis is under study.
Please checkout out other Coupe repositories in our Posgraph github organization.