Impulse Detection Convolutional Neural Network (IDCNN)

The code prepared based on the tensorflow implementation of DnCNN from https://github.com/crisb-DUT/DnCNN-tensorflow, which was designed for Gaussian noise removal. The proposed filter called IDCNN is a modyfication of the DnCNN desiged for impulsive noise removal.

Results

Under preparation

Environment

Under preparation

How to run

Under preparation

Train

• To train your own the model download BSD500 dataset from https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html
• Put images into folder data/bsd500/
• In the next step generate patches that are used in the training with default parameters

$ python generate_patches.py

or using shell script in which you can easili modify the parameters to control how the patches are generated

$ bash generate_patches.sh

• Run training with the default parameters

$ python main.py

or using shell script in which you can easili modify the training parameters

$ bash generate_patches.sh

Test

$ python main.py --phase test

Inference using the pretrained model

$ python inference.py --test_file data/img/pic003___in_40.png --save_dir .  --checkpoint_dir results/checkpoint_impulses_bsd500_41/ --phase inference

or using shell script

bash run_inference.sh