Awesome
DRT: A Lightweight Single Image Deraining Recursive Transformer
By Yuanchu Liang, Saeed Anwar, Yang Liu </br> College of Engineering and Computer Science, The Australian National University </br> Data61, CSIRO </br>
This repo is the official implementation of DRT: A Lightweight Single Image Deraining Recursive Transformer (arxiv).
The paper has been accepeted by NTIRE2022 workshop of CVPR2022.
</br>
Abstract
Over parameterization is a common technique in deep learning to help models learn and generalize sufficiently to the given task; nonetheless, this often leads to enormous network structures and consumes considerable computing resources during training. Recent powerful transformer-based deep learning models on vision tasks usually have heavy parameters and bear training difficulty. However, many dense-prediction low-level computer vision tasks, such as rain streak removing, often need to be executed on devices with limited computing power and memory in practice. Hence, we introduce a recursive local window-based self-attention structure with residual connections and propose deraining a recursive transformer (DRT), which enjoys the superiority of the transformer but requires a small amount of computing resources. In particular, through recursive architecture, our proposed model uses only ~1.3% of the number of parameters of the current best performing model in deraining while exceeding the state-of-the-art methods on the Rain100L benchmark by at least 0.33 dB. Ablation studies also investigate the impact of recursions on derain outcomes. Moreover, since the model contains no deliberate design for deraining, it can also be applied to other image restoration tasks. Our experiment shows that it can achieve competitive results on desnowing. The source code and pretrained model can be found at https://github.com/YC-Liang/DRT.
Running
Installations</br> Note the script is only tested on Ubuntu 20.04.
- Download
Python3
, script was ran byPython 3.7.11
- Install
cuda >= 10.1
withcudnn >= 7
. - Install
PyTorch >= 1.7.1
withtorchvision >= 0.8.2
- Install
timm >= 0.3.2
- Install
torch-summary >= 0.8.2
Data Sets</br>
Training: download the following data sets and unzip into the Data
folder </br>
Rain800
: click here.Rain100L-Train
: click hereRain100H-Train
: click hereSnow100K-training
: click here
Testing: inside the Data
folder, run mkdir Test-HiNet
.
- Unzip
Rain800
,Rain100L
andRain100H
into theTest-HiNet
folder: click here - Unzip
Snow100K-testset
into theData
folder: click here
The Data
folder should looks something like this:</br>
|Data
|--Rain800
|-- training
|-- ...
|-- ...
|--Rain100L-Train
|-- rain
|-- ...
|-- norain
|-- ...
|--Rain100H-Train
|-- rain
|-- ...
|-- norain
|-- ...
|--Snow100K-training
|-- all
|...
|--Test-HiNet
|-- Rain100H
|-- ...
|-- Rain100L
|-- ...
|-- Test100
|-- ...
|--Snow100K-testset
|-- Snow100K-L
|-- ...
|-- Snow100K-M
|-- ...
|-- Snow100K-S
|-- ...
Running Instructions</br>
The core content is written in the Jupyter Notebook DRT.ipynb
.</br>
To evaluate, change the evaluate folder directory in the Load Data
section and set the last line of the notebook to </br>
run(train_net = False, loadCkp = True, loadBest = True, new_dataset = False)
</br>
To train from scratch, change the training directory in the Load Data
section and set the last line of the notebook to
run(train_net = True, loadCkp = False, loadBest = False, new_dataset = False)
Results
Quantitative Results
Rain100L
Rain100H
Test100
Realistic
Citations
@InProceedings{Liang_2022_CVPR,
author = {Liang, Yuanchu and Anwar, Saeed and Liu, Yang},
title = {DRT: A Lightweight Single Image Deraining Recursive Transformer},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2022},
pages = {589-598}
}
@misc{https://doi.org/10.48550/arxiv.2204.11385,
doi = {10.48550/ARXIV.2204.11385},
url = {https://arxiv.org/abs/2204.11385},
author = {Liang, Yuanchu and Anwar, Saeed and Liu, Yang},
keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {DRT: A Lightweight Single Image Deraining Recursive Transformer},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}