Pyramid Real Image Denoising Network
This is the code for the paper "Pyramid Real Image Denoising Network". ( VCIP 2019 oral )
Paper Link : Pyramid Real Image Denoising Network
Training dataset : SIDD Medium Dataset
Validation dataset : SIDD Validation data
While deep Convolutional Neural Networks (CNNs) have shown extraordinary capability of modelling specific noiseand denoising, they still perform poorly on real-world noisyimages. The main reason is that the real-world noise is moresophisticated and diverse. To tackle the issue of blind denoising,in this paper, we propose a novel pyramid real image denoisingnetwork (PRIDNet), which contains three stages. First, the noiseestimation stage uses channel attention mechanism to recalibratethe channel importance of input noise. Second, at the multi-scale denoising stage, pyramid pooling is utilized to extractmulti-scale features. Third, the stage of feature fusion adopts akernel selecting operation to adaptively fuse multi-scale features.Experiments on two datasets of real noisy photographs demon-strate that our approach can achieve competitive performancein comparison with state-of-the-art denoisers in terms of bothquantitative measure and visual perception quality.
train_SIDD_Pyramid.py is used for training.
variable "dir_name": to store your training dataset.
variable "checkpoint_dir": to save your trained model.
variable "result_dir": to save the images produced in the training process.
test_SIDD_Pyramid.py is used for testing.
variable "val_dir": to store your testing dataset.
variable "checkpoint_dir": to store your pre-trained model.
variable "result_dir": the denoised result after testing.