The Deep iterative down-up CNN (DIDN) is a network introduced by Songhyun Yu et al. in "Deep Iterative Down-Up CNN for Image Denoising" CVPR 2019. If you use this network, please cite their work appropriately.
The official implementation is available here in Pytorch.
The goal of this implementation in TensorFlow is to be easy to read and to adapt:
- all the code is in one file
- defaults are those from the paper
- there is no other imports than from TensorFlow
Some implementation details were taken from the code and not the paper itself:
- no bias is used in the convolutions
- the number of down-up blocks is set to 6
- the activation of the last convolutional layer of the network is linear