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

Test datasets

• Real20 and Real45 datasets are provided 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

Download the code and pre-trained model

Method 1

Clone the repository by

$ git clone https://github.com/Anonymous-3000/RAGNet

and download the pretrain.pth into ./checkpoint folder by clicking the Download button in ./checkpoint/pretrain.pth .

Method 2

Install git-lfs (large file storage) by

$ git lfs install --skip-repo

and clone this repo by

$ git lfs clone https://github.com/Anonymous-3000/RAGNet

Test with the pre-trained model

$ cd RAGNet
$ python test.py