# BRN **Repository Path**: alphakappa/BRN ## Basic Information - **Project Name**: BRN - **Description**: Bilateral-Recursive-Network-for-Single-Image-Deraining - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-08-26 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README ## Bilateral Recursive Network for Single Image Deraining（ICASSP 2020） [[pdf](https://csdwren.github.io/papers/2020_icassp_BRN.pdf)] ### Introduction We in this paper propose a bilateral recurrent network (**BRN**) to simultaneously exploit the rain streak layer and the clean background image. Generally, we employ dual residual networks (**ResNet**) that are recursively unfolded to sequentially extract the rain streak layer (**Fr**) and predict the clean background image (**Fx**). In particular, we further propose bilateral LSTMs (**BLSTM**), which not only can respectively propagate deep features of the rain streak layer and the background image acorss stages, but also bring reciprocal communications between (**Fr**) and (**Fx**). The experimental results demonstrate that our BRN notably outperforms state-of-the-art deep deraining networks on synthetic datasets quantitatively and qualitatively. On real rainy images, our BRN also performs more favorably in generating visually plausible background images. ## Prerequisites - Python 3.6, PyTorch >= 0.4.0 - Requirements: opencv-python, tensorboardX - Platforms: Ubuntu 16.04, cuda-8.0 & cuDNN v-5.1 (higher versions also work well) - MATLAB for computing [evaluation metrics](statistics/) ## Datasets BRN and its variants are evaluated on three datasets*: Rain100H [1], Rain100L [1] and Rain12 [2]. Please download the testing datasets from [BaiduYun](https://pan.baidu.com/s/1J0q6Mrno9aMCsaWZUtmbkg) or [OneDrive](https://1drv.ms/f/s!AqLfQqtZ6GwGgep-hgjLxkov2SSZ3g), and place the unzipped folders into `/media/r/dataset/rain/`. To train the models, please download training datasets: RainTrainH [1] and RainTrainL [1] from [BaiduYun](https://pan.baidu.com/s/1J0q6Mrno9aMCsaWZUtmbkg) or [OneDrive](https://1drv.ms/f/s!AqLfQqtZ6GwGgep-hgjLxkov2SSZ3g), and place the unzipped folders into `/media/r/dataset/rain/`. Of course, you can also create your own folders to place datasets, but make sure it corresponds to `--data_path` in `.sh`. *_We note that:_ _(i) The datasets in the website of [1] seem to be modified. But the models and results in recent papers are all based on the previous version, and thus we upload the original training and testing datasets to [BaiduYun](https://pan.baidu.com/s/1J0q6Mrno9aMCsaWZUtmbkg) and [OneDrive](https://1drv.ms/f/s!AqLfQqtZ6GwGgep-hgjLxkov2SSZ3g)._ _(ii) For RainTrainH, we strictly exclude 546 rainy images that have the same background contents with testing images. All our models are trained on remaining 1,254 training samples._ ## Getting Started ### 1) Testing We have placed our pre-trained models into `./logs/`. Run shell scripts to test the models: ```bash bash test_BRN.sh # test models about BRN bash test_CRN.sh # test models about CRN bash test_BRN-XR.sh # test models about BRN-XR bash test_BRN-RX.sh # test models about BRN-RX ``` All the results in the paper are also available at [BaiduYun](https://pan.baidu.com/s/1KnJBNPB6Me5V464rJJT3ig&shfl=sharepset). You can place the downloaded results into `./result/`, and directly compute all the [evaluation metrics](statistics/) in this paper. ### 2) Evaluation metrics We also provide the MATLAB scripts to compute the average PSNR and SSIM values reported in the paper. ```Matlab cd ./statistic run statistic_Rain100H.m run statistic_Rain100L.m run statistic_Rain12.m ``` ### Average PSNR/SSIM values on four datasets: Dataset | BRN |BRN-XR |BRN-RX |CRN -----------|-----------|-----------|-----------|----------- Rain100H |29.58/0.902|29.50/0.901|29.16/0.898|29.10/0.897 Rain100L |37.82/0.981|37.65/0.980|37.40/0.979|37.52/0.980 Rain12 |36.70/0.959|36.63/0.959|36.54/0.959|36.58/0.959 ### 3) Training Run shell scripts to train the models: ```bash bash train_R100H.sh bash train_R100L.sh ``` You can use `tensorboard --logdir ./logs/your_model_path` to check the training procedures. ### Model Configuration The following tables provide the configurations of options. #### Training Mode Configurations Option |Default | Description -----------------------|---------------|------------ batchSize | 12 | Training batch size inter_iter | 4 | Number of recursive stages epochs | 100 | Number of training epochs milestone | [30,50,80] | When to decay learning rate lr | 1e-3 | Initial learning rate save_freq | 1 | save intermediate model use_GPU | True | use GPU or not gpu_id | 0 | GPU id data_path | N/A | path to training images outf | N/A | path to save models and status #### Testing Mode Configurations Option |Default | Description -----------------------|------------------|------------ use_GPU | True | use GPU or not gpu_id | 0 | GPU id inter_iter | 4 | Number of recursive stages logdir | N/A | path to trained model data_path | N/A | path to testing images save_path | N/A | path to save results save_path_r | N/A | path to save rain streaks ## References [1] Yang W, Tan RT, Feng J, Liu J, Guo Z, Yan S. Deep joint rain detection and removal from a single image. In IEEE CVPR 2017. [2] Li Y, Tan RT, Guo X, Lu J, Brown MS. Rain streak removal using layer priors. In IEEE CVPR 2016.