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README.md

RAGNet

  • The implementation of "Two-Stage Single Image Reflection Removal with Reflection-Aware Guidance".

Prerequisites

  • The code has been test on a PC with following environment
    • Ubuntu 18.04
    • Python 3.7.5
    • PyTorch 1.2.0
    • cudatoolkit 10.0
    • NVIDIA RTX 2080Ti

Datasets

Training datasets

Testing datasets

  • Real20: 20 real testing images from Berkeley real dataset.

  • Real45: 45 real testing images from CEILNet dataset.

  • SIR dataset: three sub-datasets (Solid, Postcard, Wild) from SIR dataset.

    We provide Real20 and Real45 in ./testsets folder, the SIR dataset is not provided due to their policy, download here and put it under ./testsets folder. Please organize the SIR dataset according to our code implementation.

Test

Test with the pre-trained model

$ cd RAGNet
$ python test.py

Train

  • Download the vgg19-pretrained model and put it into ./checkpoint folder.
  • Organize the training dataset according to our code implementation, i.e.,
$ cd synthetic
$ mkdir transmission_layer
$ mkdir blended

Start training

$ cd RAGNet
$ python train.py

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