MGCC-pytorch

Pytorch implementation of paper "Joint Image demosaicking and denoising with mutual guidance of color channels"

Installation

git clone https://github.com/yongzhangwhu/MGCC-JDD
cd MGCC-JDD  

Requirements

• Python 3.7
• PyTorch 1.7.1
• Tensorflow 1.15.0
• scipy 1.1.0
• scikit-image 0.17.2
• opencv-python

Pretrain model

You can download the pretrained models for synthetic and realistic datasets from here.

Test

• preparation

  • for synthetic datasets

    • add noise by preprocess.m using matlab for test images.
    • modify --test_noisy_path, --test_gt_path, --sigma, --pretrained_model in ./script/test-MGCC-jdd-df2k.sh.

  • for MSR dataset

    • generate txt file used for test by ./datasets/generate_image_list.py.
    • modify --test_datalist, --pretrained_model in ./script/test-MGCC-jdd-df2k_msr.sh.
• test model
  • test model trained by synthesis datasets

    sh ./script/test-MGCC-jdd-df2k.sh  
    

  • test model trained by MSR datasets

    sh ./script/test-MGCC-jdd-df2k_msr.sh 
    

Train

We train our model on both synthesis datasets(DF2k) and realistic dataset(MSR dataset).

• preparation
  • generate txt file used for training by ./datasets/generate_image_list.py.
  • modify the training setting in ./script/run-MGCC-jdd-df2k.sh or ./script/run-MGCC-jdd-df2k_msr.sh.
• train model
  • on synthetic datasets

      sh ./script/run-MGCC-jdd-df2k.sh
    

  • on realistic dataset

      sh ./script/run-MGCC-jdd-df2k_msr.sh
    

Sample results

Acknowlegements

The architecture of our codes are based on TENet. The pac code is provided by PACNet. The noise estimation code wmad_estimator.py is provided by Kokkinos.