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README.md

Nanoscale Microscopy Images Colourization Using Neural Networks

Project | Arxiv | Dataset | Dataset_2

Keras implementation for learning a mapping from SEM gray images to colorful images, for example:

Nanoscale Microscopy Images Colourization Using Neural Networks

Note: Please check out our Tensorflow implementation for End2End ColorNet(End2End.ipynb) and CNN-NST(cnn_nst.ipynb).

Setup

Prerequisites

  • Linux or OSX
  • NVIDIA GPU + CUDA CuDNN (CPU mode and CUDA without CuDNN may work with minimal modification, but untested)
  • Keras with tensorflow backend

Getting Started

  • Install keras with tensorflow backend and dependencies from https://keras.io/#installation
  • Install python packages jupyter-notebook, scikit-image and opencv
pip install scikit-image
pip install jupyter
pip install opencv-python
  • Install livelossplot(optional) - a live monitor during training.

  • Clone this repo:

git clone https://github.com/isrugeek/semcolour
cd semcolour

End2End Training Network

check the code in End2End.ipynb.

Train

Setting the right gpu configure. ex:

os.environ["CUDA_VISIBLE_DEVICES"]="0,1,2"

Variable iteration means epochs, change these settings for your own conditions.

# global varibles
trainset_path = "datasets/trainset"
devset_path = "datasets/devset"
testset_path = "datasets/testset"
batch_size = 16
# epochs
iteration = 100

target_size = (256, 256)

Test

Run the last cell in the End2End.ipynb file to test the datasets which in devset_path and save these to results directory.

Note

flow_from_directory_regress.py is rewrite the class ImageDataGenerator, adding register_batch_process method to make our preprocess process more convenient, make sure u have this py file in your work directory.

Datasets

We collected 800 colorful sem-images as SEMCOLORFUL1.0 in the datasets directory split into devset and trainset.

Models

The pre-trained models pretrained_end2end_model.h5 can be loaded by running follow line.

model.load_weights('pretrained_end2end_model.h5')

after load the pretrained weights, you can test the model directly instead of training.

CNN neural style transfer Network

Train & Test

Check the code in cnn_nst.ipynb. reference_img indicates the reference images, dir_sem is the directory which contains SEM gray images, sem_input indicates specific the input gray sem image. It doesn't like traditional neural style transfer process, It just learn how to map reference image from gray to color by CNN, then using the weights of the CNN to map the SEM input image to colorful image. same_grey_same_colorize function can provide a constraint to put same color on the parts which are same gray level,it can correct some CNN's mistakes.

Citation

If you use this code for your research, please cite our paper Nanoscale Microscopy Images Colourization Using Neural Networks :

@article{goytom2019nanoscale,
  title={Nanoscale Microscopy Images Colourisation Using Neural Networks},
  author={Goytom, Israel and Wang, Qin and Yu, Tianxiang and Sankaran, Kris and Lin, Dongdong},
  journal={arXiv preprint arXiv:1912.07964},
  year={2019}
}