liuguoyou / coupe.unsupervised-color-enhance

Unsupervised image color enhancement using implementation of CycleGAN-tensorflow

Unsupervised Color Enhancement

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.

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Prerequisites

• tensorflow r1.0 or higher version
• numpy 1.11.0
• scipy 0.17.0
• pillow 3.3.0

Getting Started

Installation

• Install tensorflow from https://github.com/tensorflow/tensorflow
• Clone this repo:

git clone https://github.com/JunhoJeon/unsupervised-color-enhance
cd CycleGAN-tensorflow

Main Files

• 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

Training and Test Details

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

Reference

• 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

License

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.

About Coupe Project

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.

Useful Links

• Coupe Library
• POSTECH CG Lab.