CBDNet-pytorch
It's an unofficial PyTorch implementation of CBDNet.
We used higher quality real and synthetic datasets for training and achieved better performance on DND.
Quick Start
Download the dataset and pretrained model from GoogleDrive.
Extract the files to data folder and save_model folder as follow:
~/
data/
SIDD_train/
... (scene id)
Syn_train/
... (id)
DND/
images_srgb/
... (mat files)
... (mat files)
save_model/
checkpoint.pth.tar
Train the model:
python train.py
Predict using the trained model:
python predict.py input_filename output_filename
Network Structure
Realistic Noise Model
Given a clean image x, the realistic noise model can be represented as:
Where y is the noisy image, f(.) is the CRF function and the irradiance ,
M(.) represents the function that convert sRGB image to Bayer image and DM(.) represents the demosaicing function.
If considering denosing on compressed images,
Result
