/Reflection-Removal

Class project of computational photography, including the implementation and improvement of CVPR 2019 paper "Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements"

Primary LanguagePython

Class project of computational photography

The implementation and improvement of CVPR 2019 paper "Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements"

Prerequisites

  • Python >=3.5, PyTorch >= 0.4.1
  • Requirements: opencv-python, tensorboardX, visdom
  • Platforms: Ubuntu 16.04, cuda-8.0

Folder list

  • ERRNet-ref folder contains the debugged original code of ERRNet
  • ERRNet-mydata folder contains the code for training using our own synthetic data (Code for data generating is in sync_model folder）
  • ERRNet-TR folder contains our newly proposed network: ERRNet-TR and the relating training and test code
  • ERRNet-TR-lite folder contains our newly proposed network: ERRNet-TR-lite and the relating training and test code
  • sync_model folder contains code for data generating and a data sample

Quick Start

Testing

  • Running the following command after entering the corresponding folder python test_errnet.py --name errnet -r --icnn_path checkpoints/errnet/ourmodel.pt --hyper --gpu_ids -1 (gpu_ids=-1 means using CPU to test)

Training

  • Running the following command after entering the corresponding folder python train_errnet.py --name errnet --hyper （Need to prepare the dataset in the folder reflection_data before training）
  • Viewing options/errnet/train_options.py for more training options