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| Cover figure | Network structure |
Restoring Images in Adverse Weather Conditions via Histogram Transformer
Shangquan Sun, Wenqi Ren, Xinwei Gao, Rui Wang, Xiaochun Cao
European Conference on Computer Vision
Abstract (click to expand)
Transformer-based image restoration methods in adverse weather have achieved significant progress. Most of them use self-attention along the channel dimension or within spatially fixed-range blocks to reduce computational load. However, such a compromise results in limitations in capturing long-range spatial features. Inspired by the observation that the weather-induced degradation factors mainly cause similar occlusion and brightness, in this work, we propose an efficient Histogram Transformer (Histoformer) for restoring images affected by adverse weather. It is powered by a mechanism dubbed histogram self-attention, which sorts and segments spatial features into intensity-based bins. Self-attention is then applied across bins or within each bin to selectively focus on spatial features of dynamic range and process similar degraded pixels of the long range together. To boost histogram self-attention, we present a dynamic-range convolution enabling conventional convolution to conduct operation over similar pixels rather than neighbor pixels. We also observe that the common pixel-wise losses neglect linear association and correlation between output and ground-truth. Thus, we propose to leverage the Pearson correlation coefficient as a loss function to enforce the recovered pixels following the identical order as ground-truth. Extensive experiments demonstrate the efficacy and superiority of our proposed method.If you use Histoformer, please consider citing:
@article{sun2024restoring,
title={Restoring Images in Adverse Weather Conditions via Histogram Transformer},
author={Sun, Shangquan and Ren, Wenqi and Gao, Xinwei and Wang, Rui and Cao, Xiaochun},
journal={arXiv preprint arXiv:2407.10172},
year={2024}
}
- 2024.08.29: Update alternative [Download Links] for Snow100K masks as its original link expires.
- 2024.08.29: Update alternative [Download Links] for Outdoor-Rain Test1 as its original link expires.
- 2024.07.23: Update alternative [Download Links] for Snow100K as its original link expires.
- 2024.07.18: Codes and pre-trained weights are released.
- 2024.07.17: Visual results are released.
- 2024.07.14: Arxiv Paper is released.
- 2024.07.01: Histoformer is accepted by ECCV2024.
- 2024.03.01: Histoformer is rejected by CVPR2024.
According to [Issue#2] and [Issue#8], many related datasets are unavailable. I have provided them below:
| Snow100K Training | Snow100K Test Set | Snow100K Masks | Outdoor-Rain Test1 |
|---|---|---|---|
| [Google Drive] | [Google Drive] [BaiduYun Disk] [pin: yuia] |
[Google Drive] [BaiduYun Disk] [pin: hstm] |
[Google Drive] |
All visual results are in Google Drive and Baidu Disk (pin: ps9q).
You can also find each of them from the table below.
| Method | RainDrop | Outdoor-Rain | Snow100K-L | Snow100K-S | RealSnow |
|---|---|---|---|---|---|
| TransWeather | Results | Results | Results | Results | Results |
| WGWSNet | Results | Results | Results | Results | Results |
| Chen et al. | Results | Results | Results | Results | Results |
| WeatherDiff | Results | Results | Results | Results | Results |
| Histoformer | Results | Results | Results | Results | Results |
Examples:
 |
 |
|---|---|
| RainDrop | Outdoor-Rain |
 |
 |
|---|---|
| Snow100K-L | RealSnow |
See INSTALL.md for the installation of dependencies required to run Histoformer.
- Download Training set or each of them, i.e., Snow100K, Outdoor-Rain, and RainDrop.
Note: The original link for downloading Snow100K has expired, so you could refer to [Issue#2] for alternative download links.
-
Modify the configurations of
dataroot_gtanddataroot_lqfortrain,val_snow_s,val_snow_l,val_test1andval_raindropin Allweather/Options/Allweather_Histoformer.yml -
To train Histoformer with default settings, run
cd Histoformer
./train.sh Allweather/Options/Allweather_Histoformer.yml 4321
Note: The above training script uses 4 GPUs by default.
To use any other settings, for example 8 GPUs, modify CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 and --nproc_per_node=8 in Histoformer/train.sh and num_gpu: 8 Allweather/Options/Allweather_Histoformer.yml.
-
cd Allweather -
Download the pre-trained models and place it in
./pretrained_models/ -
Download test datasets from each of them, i.e., Snow100K, Outdoor-Rain, and RainDrop.
-
Test with the replaced argument
--input_dir [INPUT_FOLDER]
python test_histoformer.py --input_dir [INPUT_FOLDER] --result_dir result/ --weights pretrained_models/net_g_best.pth --yaml_file Options/Allweather_Histoformer.yml
# for realsnow
python test_histoformer.py --input_dir [INPUT_FOLDER] --result_dir result/ --weights pretrained_models/net_g_real.pth --yaml_file Options/Allweather_Histoformer.yml
- Compute PSNR and SSIM by
python compute_psnr.py --path1 [GT-PATH] --path2 [Restored-PATH]
Values may be slightly different because a) the images I upload are in JPG file format for saving space, but the values reported are computed on the PNG-format images; b) some values are reported by previous works like WeatherDiff and may be slightly different from this reproduction.
-
cd Allweather -
Download the pre-trained models and place it in
./pretrained_models/ -
Test with the replaced argument
--input_dir [INPUT_FOLDER]
# for realsnow
python test_histoformer.py --input_dir [INPUT_FOLDER] --result_dir result/ --weights pretrained_models/net_g_real.pth --yaml_file Options/Allweather_Histoformer.yml
If you have any question, please contact [email protected]
Acknowledgment: This code is based on the WeatherDiff, Restormer, BasicSR toolbox and HINet.
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