Selective Wavelet Attention Learning for Single Image Deraining

Last update: Jun 17, 2022

Overview

SWAL

Code for Paper "Selective Wavelet Attention Learning for Single Image Deraining"

Prerequisites

Python 3
PyTorch

Models

We provide the models trained on DDN, DID, Rain100H, Rain100L, and AGAN datasets in the following links:

Download them into the model folder before testing.

Dataset

Download the rain datasets.
Arrange the images and generate a list file, just like the rain12 set in the data folder.

You can also modify the data_loader code in your manner.

Run

Train SWAL on a single GPU:

 CUDA_VISIBLE_DEVICES=0 python main.py --ngf=16 --ndf=64  --output_height=320  --trainroot=YOURPATH --trainfiles='YOUR_FILELIST'  --save_iter=1 --batchSize=8 --nrow=8 --lr_d=1e-4 --lr_g=1e-4  --cuda  --nEpochs=500

Train SWAL on multiple GPUs:

 CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --ngf=16 --ndf=64  --output_height=320  --trainroot=YOURPATH --trainfiles='YOUR_FILELIST'  --save_iter=1 --batchSize=32 --nrow=8 --lr_d=1e-4 --lr_g=1e-4  --cuda  --nEpochs=500

Test SWAL:

 CUDA_VISIBLE_DEVICES=0 python test.py --ngf=16  --outf='test' --testroot='data/rain12_test' --testfiles='data/rain12_test.list' --pretrained='model/rain100l_best.pth'  --cuda

Adjust the parameters according to your own settings.

Citation

If you use our codes, please cite the following paper:

 @article{huang2021selective,
   title={Selective Wavelet Attention Learning for Single Image Deraining},
   author={Huang, Huaibo and Yu, Aijing and Chai, Zhenhua and He, Ran and Tan, Tieniu},
   journal={International Journal of Computer Vision},
   volume={129},
   number={4},
   pages={1282--1300},
   year={2021},
  }

The released codes are only allowed for non-commercial use.

Selective Wavelet Attention Learning for Single Image Deraining

Related tags

Overview

SWAL

Prerequisites

Models

Dataset

Run

Citation

Owner

Bobo

Discover hidden deepweb pages

A tiny, friendly, strong baseline code for Person-reID (based on pytorch).

Changing the Mind of Transformers for Topically-Controllable Language Generation

The official implementation of paper Siamese Transformer Pyramid Networks for Real-Time UAV Tracking, accepted by WACV22

Commonality in Natural Images Rescues GANs: Pretraining GANs with Generic and Privacy-free Synthetic Data - Official PyTorch Implementation (CVPR 2022)

Tensorflow implementation of "Learning Deconvolution Network for Semantic Segmentation"

Code for testing convergence rates of Lipschitz learning on graphs

Codes for our IJCAI21 paper: Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization

Implementation for the IJCAI2021 work "Beyond the Spectrum: Detecting Deepfakes via Re-synthesis"

Training PSPNet in Tensorflow. Reproduce the performance from the paper.

PyTorch Language Model for 1-Billion Word (LM1B / GBW) Dataset

Potato Disease Classification - Training, Rest APIs, and Frontend to test.

transfer attack; adversarial examples; black-box attack; unrestricted Adversarial Attacks on ImageNet; CVPR2021 天池黑盒竞赛

Train DeepLab for Semantic Image Segmentation

This is an official implementation of the CVPR2022 paper "Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spots".

Source code, datasets and trained models for the paper Learning Advanced Mathematical Computations from Examples (ICLR 2021), by François Charton, Amaury Hayat (ENPC-Rutgers) and Guillaume Lample

Code for the prototype tool in our paper "CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning".

Modified fork of Xuebin Qin's U-2-Net Repository. Used for demonstration purposes.

FedScale: Benchmarking Model and System Performance of Federated Learning

Code examples and benchmarks from the paper "Understanding Entropy Coding With Asymmetric Numeral Systems (ANS): a Statistician's Perspective"