This is the official implementation of the paper titled "Deep Perceptual Enhancement for Medical Image Analysis. [Click Here].
Please consider citing this paper as follows:
@article{sharif2022deep,
title={Deep Perceptual Enhancement for Medical Image Analysis},
author={Sharif, SMA and Naqvi, Rizwan Ali and Biswas, Mithun and Loh, Woong-Kee},
journal={IEEE Journal of Biomedical and Health Informatics},
year={2022},
publisher={IEEE}
}
Medical image acquisition devices are susceptible to producing low-quality (i.e., low contrast, inappropriate brightness, noise, etc.) images. Our work aims to comprehensively enhance low-quality images by incorporating end-to-end learning strategies for accelerating medical image analysis tasks.
Python 3.8
CUDA 10.1 + CuDNN
pip
Virtual environment (optional)
Please consider using a virtual environment to continue the installation process.
git clone https://github.com/sharif-apu/DPE_JBHI.git
cd DPE_JBHI
pip install -r requirement.txt
Place all training images into a unified directory. Please refer to the original article for the reference datasets. Low-quality images will be automatically generated during training. To specify the path of your training image, go to mainModule/config.json and update "gtPath" and "targetPath" entities (both paths should be the same).
To start training please execute the following command:
```python main.py -ts -e X -b Y```
You can specify the number of epoch with **-e** flag (i.e., -e 5) and number of images per batch with **-b** flag (i.e., -b 12).
execute
To inference with custom setting execute the following command:
python main.py -i -s path/to/inputImages -d path/to/outputImages
Here, -s specifies the root directory of the source images (i.e., testingImages/), and -d specifies the destination root (i.e., modelOutput/).
For transfer learning/ resume code execute:
python main.py -tr -e -b
For any further query, feel free to contact us through the following emails: sma.sharif.cse@ulab.edu.bd, rizwanali@sejong.ac.kr, or mithun.bishwash.cse@ulab.edu.bd