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Real Image Denoising

Training

  • Download SIDD training data, run
python download_data.py --data train --noise real
  • Generate image patches from full-resolution training images, run
python generate_patches_sidd.py
  • Train MIRNet_v2
cd MIRNetv2
./train.sh Real_Denoising/Options/RealDenoising_MIRNet_v2.yml

Note: This training script uses 8 GPUs by default. To use any other number of GPUs, modify MIRNetv2/train.sh and Real_Denoising/Options/RealDenoising_MIRNet_v2.yml

Evaluation

  • Download the pre-trained model and place it in ./pretrained_models/:
wget https://github.com/swz30/MIRNetv2/releases/download/v1.0.0/real_denoising.pth -P pretrained_models/

Testing on SIDD dataset

  • Download SIDD validation data, run
python download_data.py --noise real --data test --dataset SIDD
  • To obtain denoised results, run
python test_real_denoising_sidd.py --save_images
  • To reproduce PSNR/SSIM scores on SIDD data (Table 3), run
evaluate_sidd.m

Testing on DND dataset

  • Download the DND benchmark data, run
python download_data.py --noise real --data test --dataset DND
  • To obtain denoised results, run
python test_real_denoising_dnd.py --save_images