ResidualDenseNetWork-CycleGAN-SuperResolution

Using Residual Dense Network as one of Generator and define another RDN within a downsampler

Pytorch implement: Residual Dense Network for Image Super-Resolution
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

Different with the paper 1, I use there RDBs(Residual dense block), every RDB has three dense layers. So ,this is a sample implement the RDN(Residual Dense Network) proposed by the author.

Different with the paper 2, I use paired training data to constrain the convert performance.

Requirements

• python3.6
• pytorch >= 1.0
• torchvision >= 2.1
• opencv

Dateset

you need prepare DIV2K dataset Download the High Resolution and (2x/3x/4x) Low Resolution put them in ./DIV2K

References

ResidualDenseNetwork-Pytorch

pytorch-CycleGAN-and-pix2pix