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

Temporal Knowledge Graph Reasoning Triggered by Memories

This is the repo for Uncertainty Quantification 360 Toolkit.

Learn about Spice.ai with in-depth samples

This repository contains the scripts for downloading and validating scripts for the documents

Rethinking Transformer-based Set Prediction for Object Detection

converts nominal survey data into a numerical value based on a dictionary lookup.

Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation

Single object tracking and segmentation.

HybVIO visual-inertial odometry and SLAM system

Implementation of popular bandit algorithms in batch environments.

Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection, AAAI 2021.

Real-time LIDAR-based Urban Road and Sidewalk detection for Autonomous Vehicles 🚗

Semi-supevised Semantic Segmentation with High- and Low-level Consistency

ServiceX Transformer that converts flat ROOT ntuples into columnwise data

Deep Learning Theory

A modified version of DeepMind's Alphafold2 to divide CPU part (MSA and template searching) and GPU part (prediction model)

[NeurIPS 2021] Source code for the paper "Qu-ANTI-zation: Exploiting Neural Network Quantization for Achieving Adversarial Outcomes"

Optimized Gillespie algorithm for simulating Stochastic sPAtial models of Cancer Evolution (OG-SPACE)

paper list in the area of reinforcenment learning for recommendation systems

FS-Mol: A Few-Shot Learning Dataset of Molecules