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

Behind the Curtain: Learning Occluded Shapes for 3D Object Detection

Project repo for Learning Category-Specific Mesh Reconstruction from Image Collections

Simple renderer for use with MuJoCo (>=2.1.2) Python Bindings.

Code associated with the paper "Deep Optics for Single-shot High-dynamic-range Imaging"

Pytorch implementation of Learning with Opponent-Learning Awareness

codes for Self-paced Deep Regression Forests with Consideration on Ranking Fairness

Keras implementation of Deeplab v3+ with pretrained weights

The codes and related files to reproduce the results for Image Similarity Challenge Track 2.

Connecting Java/ImgLib2 + Python/NumPy

BlueFog Tutorials

Official implementation for paper Knowledge Bridging for Empathetic Dialogue Generation (AAAI 2021).

Official Code Release for Container : Context Aggregation Network

Depth-Aware Video Frame Interpolation (CVPR 2019)

Face-Recognition-based-Attendance-System - An implementation of Attendance System in python.

A tensorflow/keras implementation of StyleGAN to generate images of new Pokemon.

Benchmark for evaluating open-ended generation

Official Repsoitory for "Activate or Not: Learning Customized Activation." [CVPR 2021]

TorchMetrics is a collection of 25+ PyTorch metrics implementations and an easy-to-use API to create custom metrics.

Finetuner allows one to tune the weights of any deep neural network for better embeddings on search tasks

Deep Learning as a Cloud API Service.