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

If you like this repo, Star or Fork to support my work. Thank you.