With this Image Enhancer, you can:
- reduce image noise
- color a grayscale image
- enhance resolution ratio
- and so on
Anything that in the process of corrupt - restore can be trained.
Image source: Pascal VOC 2012, Image shape: 128, 128, 3
Learning rate: 0.001, Batch size: 128, Epoch: 50
Trained on NVIDIA Tesla K40c (12GB)
| Corruption type | Ratio |
|---|---|
| GSN (Gaussian Noise) | 2% |
| Corruption type | Ratio |
|---|---|
| GSB (Gaussian Blur) | 2x |
| Corruption type | Ratio |
|---|---|
| BLK | 25 |
| Corruption type | Ratio |
|---|---|
| GRY | N/A |
| Corruption type | Ratio |
|---|---|
| ZIP | N/A |
Simplest (input directory and image shape are required)
default corruption type is GSN, default ratio is 0.02
python train.py -i ~/data/images/ -s 128 128 3
Specify hyperparameters (learning rate, batch size, epoches, activation function)
default learning rate is 0.001, batch size is 128, epoch is 50, activation function is relu
python train.py -i ~/data/images/ -s 128 128 3 -r 0.001 -b 64 -e 100 -a prelu
Specify corruption types and/or ratio
python train.py -i ~/data/images/ -s 128 128 3 -T ZIP
python train.py -i ~/data/images/ -s 128 128 3 -T GSN -R 0.05
Till now, there are several corruption types:
- GSN Gaussian Noise, -R defines noise ratio
- MSN Masking Noise, -R defines noise ratio
- SPN Salt & Pepper Noise, -R defines noise ratio
- GSB Gaussian Blur, -R defines blur ratio
- BLK Put a black circle on a random position of raw image, -R defines circle radius
- GRY Transform RGB image to grayscale image (raw image should be RGB)
- ZIP Zip image as half of original resolution
Specify file path
python train.py -i ~/data/images/ -s 128 128 3 --graph-path ./graphs/ --checkpoint-path ./checkpoints/ --example-path ./examples/
Save checkpoints with best validation results only
by default, both best checkpoint and checkpoints of each epoch will be saved, set this flag can reduce the usage of storage
python train.py -i ~/data/images/ -s 128 128 3 --best-only
Restore saved model from checkpoint files and continue training
python train.py -i ~/data/images/ -s 128 128 3 --restore --checkpoint-path ./checkpoints/ --checkpoint-name checkpoint.best.hdf5
Training on CPU
python train.py -i ~/data/images/ -s 128 128 3 --cpu-only
Simplest (input directory and image shape are required)
default checkpoint file is ./checkpoints/checkpoint.best.hdf5
python process.py -i ~/data/images/ -s 128 128 3
Specify batch size
default is 128
python process.py -i ~/data/images/ -s 128 128 3 -b 64
Specify checkpoint path and/or name
python process.py -i ~/data/images/ -s 128 128 3 --checkpoint-path ./checkpoints/ --checkpoint-name checkpoint.20-0.55.hdf5
Processing on CPU
python process.py -i ~/data/images/ -s 128 128 3 --cpu-only
Main reference:
@article{Dong:2016,
author = {Dong, Jianfeng and Mao, Xiao-Jiao and Shen, Chunhua and Yang, Yu-Bin},
title = {Unsupervised Feature Learning With Symmetrically Connected Convolutional Denoising Auto-encoders},
journal = {CoRR},
volume = {abs/1611.09119},
url = {http://arxiv.org/abs/1611.09119},
year = {2016},
type = {Journal Article}
}
Some papers about autoencoder and denoising autoencoder:
@article{Vincent:2010,
author = {Vincent, Pascal and Larochelle, Hugo and Lajoie, Isabelle and Bengio, Yoshua and Manzagol, Pierre-Antoine},
title = {Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion},
journal = {J. Mach. Learn. Res.},
volume = {11},
pages = {3371-3408},
ISSN = {1532-4435},
year = {2010},
type = {Journal Article}
}
@inproceedings{Germain:2015,
author = {Germain, Mathieu and Gregor, Karol and Murray, Iain and Larochelle, Hugo},
title = {MADE: Masked Autoencoder for Distribution Estimation},
booktitle = {Proceedings of the 32Nd International Conference on International Conference on Machine Learning - Volume 37},
series = {ICML'15},
year = {2015},
location = {Lille, France},
pages = {881--889},
numpages = {9},
url = {http://dl.acm.org/citation.cfm?id=3045118.3045213},
acmid = {3045213},
publisher = {JMLR.org},
}
Some famous CNN papers:
@article{Krizhevsky:2017,
author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E.},
title = {ImageNet Classification with Deep Convolutional Neural Networks},
journal = {Commun. ACM},
issue_date = {June 2017},
volume = {60},
number = {6},
month = may,
year = {2017},
issn = {0001-0782},
pages = {84--90},
numpages = {7},
url = {http://doi.acm.org/10.1145/3065386},
doi = {10.1145/3065386},
acmid = {3065386},
publisher = {ACM},
address = {New York, NY, USA},
}
@article{Simonyan:2014,
author = {Simonyan, Karen and Zisserman, Andrew},
title = {Very Deep Convolutional Networks for Large-Scale Image Recognition},
journal = {CoRR},
volume = {abs/1409.1556},
year = {2014},
url = {http://arxiv.org/abs/1409.1556},
archivePrefix = {arXiv},
eprint = {1409.1556},
timestamp = {Wed, 07 Jun 2017 14:41:51 +0200},
biburl = {http://dblp.org/rec/bib/journals/corr/SimonyanZ14a},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
Deep Residual Network:
@INPROCEEDINGS{He:2016,
author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
booktitle={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
title={Deep Residual Learning for Image Recognition},
year={2016},
volume={},
number={},
pages={770-778},
keywords={image classification;learning (artificial intelligence);neural nets;object detection;CIFAR-10;COCO object detection dataset;COCO segmentation;ILSVRC & COCO 2015 competitions;ILSVRC 2015 classification task;ImageNet dataset;ImageNet localization;ImageNet test set;VGG nets;deep residual learning;deep residual nets;deeper neural network training;image recognition;residual function learning;residual nets;visual recognition tasks;Complexity theory;Degradation;Image recognition;Image segmentation;Neural networks;Training;Visualization},
doi={10.1109/CVPR.2016.90},
ISSN={},
month={June}
}