Image interpolation with auto-encoder, TensorFlow implementation.

Real world data is noisy. Noise can come in the form of incorrect or missing values. This code is supplementary to an article [1], describing a way of filling missing values with auto-encoders. An auto-encoder is trained on noisy data with no reference values available for missing entries. The procedure is explained on the task of image reconstruction. If using the code please cite [2].

[1] https://medium.com/@aliaksei.mikhailiuk/unsupervised-learning-for-data-interpolation-e259cf5dc957

[2] Mikhailiuk A. and Faul A., 2018. "Deep Learning Applied to Seismic Data Interpolation". In: European Association of Geoscientists and Engineers (EAGE) 2018. IEEE

In order to run the code:

1. Specify train, test and validation images in ./data/dataset.csv

   • can have multiple images (every line in the file corresponds to a new image)
   • The format:
     • image_noisy: image to train on (e.g. ./data/noisy_images/0_70.bmp is simply 0_70), 0 - image number, 70 - amount of noise
     • mask_train: mask for the noisy image - specifies position of the missing values
     • mask_validation: validation mask
     • image_test: test image to reconstruct with the trained network
     • mask_test: mask specifiying missing positions of pixels in the test image

2. Activate tensorflow

3. python3 main.py

4. Results are saved in ./data/reconstructed/