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  • Created over 5 years ago
  • Updated over 2 years ago

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Repository Details

Reimplement of 'Burst Denoising with Kernel Prediction Networks' and 'Multi-Kernel Prediction Networks for Denoising of Image Burst' by using PyTorch

Kernel Prediction Networks and Multi-Kernel Prediction Networks

Reimplement of Burst Denoising with Kernel Prediction Networks and Multi-Kernel Prediction Networks for Denoising of Image Burst by using PyTorch.

The partial work is following https://github.com/12dmodel/camera_sim.

TODO

Write the documents.

Requirements

  • Python3
  • PyTorch >= 1.0.0
  • Scikit-image
  • Numpy
  • TensorboardX (needed tensorflow support)

How to use this repo?

Firstly, you can clone this repo. including train and test codes, and download pretrained model at https://drive.google.com/open?id=1Xnpllr1dinAU7BIN21L3LkEP5AqMNWso.

The repo. supports multiple GPUs to train and validate, and the default setting is multi-GPUs. In other words, the pretrained model is obtained by training on multi-GPUs.

  • If you want to restart the train process by yourself, the command you should type is that
CUDA_VISIBLE_DEVICES=x,y train_eval_sym.py --cuda --mGPU -nw 4 --config_file ./kpn_specs/kpn_config.conf --restart

If no option of --restart, the train process could be resumed from when it was broken.

  • If you want to evaluate the network by pre-trained model directly, you could use
CUDA_VISIBLE_DEVICES=x,y train_eval_syn.py --cuda --mGPU -nw 4 --eval

If else option -ckpt is choosen, you can select the other models you trained.

  • Anything else.
    • The code for single image is not released now, I will program it in few weeks.

Results

The following images and more examples can be found at here.

Ground Truth Noisy Denoised
Ground Truth Noisy Denoised
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