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[arXiv 2026] UDPNet: Unleashing Depth-based Priors for Robust Image Dehazing

UDPNet 최신버젼 다운로드

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# :fire: UDPNet: Unleashing Depth-based Priors for Robust Image Dehazing

This repository contains the official implementation of the following paper:
> UDPNet: Unleashing Depth-based Priors for Robust Image Dehazing 
> [Zengyuan Zuo](), [Junjun Jiang](http://homepage.hit.edu.cn/jiangjunjun)*, [Gang Wu](https://me.csgwu.site/), [Xianming Liu](http://homepage.hit.edu.cn/xmliu)

[AIIA Lab](https://aiialabhit.github.io/team/), Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China.

**Paper Link:** [[official link](https://arxiv.org/abs/2601.06909)]

## Overview
![overall_structure](https://raw.githubusercontent.com/Harbinzzy/UDPNet/main/figs/pipeline.jpg)
>Image dehazing has witnessed significant advancements with the development of deep learning models. However, a few methods predominantly focus on single-modal RGB features, neglecting the inherent correlation between scene depth and haze distribution. Even those that jointly optimize depth estimation and image dehazing often suffer from suboptimal performance due to inadequate utilization of accurate depth information. In this paper, we present UDPNet, a general framework that leverages depth-based priors from large-scale pretrained depth estimation model DepthAnything V2 to boost existing image dehazing models. Specifically, our architecture comprises two typical components: the Depth-Guided Attention Module (DGAM) adaptively modulates features via lightweight depth-guided channel attention, and the Depth Prior Fusion Module (DPFM) enables hierarchical fusion of multi-scale depth map features by dual sliding-window multi-head cross-attention mechanism. These modules ensure both computational efficiency and effective integration of depth priors. Moreover, the intrinsic robustness of depth priors empowers the network to dynamically adapt to varying haze densities, illumination conditions, and domain gaps across synthetic and real-world data. Extensive experimental results demonstrate the effectiveness of our UDPNet, outperforming the state-of-the-art methods on popular dehazing datasets, such as 0.85 dB PSNR improvement on the SOTS dataset, 1.19 dB on the Haze4K dataset and 1.79 dB PSNR on the NHR dataset.

:star: If UDPNet is helpful to your projects, please help star this repo. Thank you! :point_left:

## Installation
The project is built with PyTorch 3.8, PyTorch 1.8.1. CUDA 10.2, cuDNN 7.6.5
For installing, follow these instructions:
~~~
conda install pytorch=1.8.1 torchvision=0.9.1 -c pytorch
pip install tensorboard einops scikit-image pytorch_msssim opencv-python
conda install pillow
~~~
**Please use the *pillow* package downloaded by Conda instead of pip.**

Install warmup scheduler:
~~~
cd pytorch-gradual-warmup-lr/
python setup.py install
cd ..
~~~

## Pretrained models

**Download Links (Dehazing and All-in-One Image Restoration):** [Baidu Netdisk](https://pan.baidu.com/s/1JqB-YBPzZAiQsdLlNcidLQ?pwd=2026) password: 2026

## Results

📊 Synthetic Benchmarks
<details> <summary>Table 1. SOTS-Indoor & SOTS-Outdoor</summary>

| Method                  | Venue    | Indoor PSNR | Indoor SSIM | Outdoor PSNR | Outdoor SSIM |
| ----------------------- | -------- | ----------- | ----------- | ------------ | ------------ |
| DCP                     | TPAMI'10 | 16.61       | 0.855       | 19.14        | 0.861        |
| FFA-Net                 | AAAI'20  | 36.39       | 0.989       | 33.57        | 0.984        |
| AECR-Net                | CVPR'21  | 37.17       | 0.990       | -            | -            |
| DeHamer                 | CVPR'22  | 36.63       | 0.988       | 35.18        | 0.986        |
| DehazeFormer-L          | TIP'23   | 40.05       | 0.996       | -            | -            |
| FSNet **(Baseline)**    | TPAMI'23 | 42.45       | 0.997       | 40.40        | 0.997        |
| MB-TaylorFormer-L       | ICCV'23  | 42.64       | 0.994       | 38.09        | 0.991        |
| FocalNet                | ICCV'23  | 40.82       | 0.996       | 37.71        | 0.995        |
| C²PNet                  | CVPR'23  | 42.56       | 0.995       | 36.68        | 0.990        |
| DEA-Net-CR              | TIP'24   | 41.31       | 0.995       | 36.59        | 0.990        |
| DCMPNet                 | CVPR'24  | 42.18       | 0.997       | 36.56        | 0.993        |
| GridFormer              | IJCV'24  | 42.34       | 0.994       | -            | -            |
| ConvIR-B **(Baseline)** | TPAMI'24 | 42.72       | 0.997       | 39.42        | 0.996        |
| SFMN                    | TIP'25   | 41.44       | 0.995       | 37.72        | 0.991        |
| PoolNet-B               | TIP'25   | 42.01       | 0.997       | -            | -            |
| PGH²Net                 | AAAI'25  | 41.70       | 0.996       | 37.52        | 0.989        |
| MB-TaylorFormerV2-L     | TPAMI'25 | 42.84       | 0.995       | 39.25        | 0.992        |
| **ConvIR + UDP (Ours)** | –        | 43.12       | 0.997       | 40.32        | 0.996        |
| **FSNet + UDP (Ours)**  | –        | **43.30**   | **0.997**   | **40.53**    | **0.997**    |

</details>

<details> <summary>Table 2. Haze4K</summary>

| Method                  | PSNR      | SSIM     |
| ----------------------- | --------- | -------- |
| DehazeNet               | 19.12     | 0.84     |
| GridDehazeNet           | 23.29     | 0.93     |
| FFA-Net                 | 26.96     | 0.95     |
| DMT-Net                 | 28.53     | 0.96     |
| PMNet                   | 33.49     | 0.98     |
| MB-TaylorFormer-L       | 34.47     | 0.99     |
| FSNet **(Baseline)**    | 34.12     | 0.99     |
| GridFormer              | 33.27     | 0.99     |
| ConvIR-B **(Baseline)** | 34.15     | 0.99     |
| DEA-Net-CR              | 34.25     | 0.99     |
| MB-TaylorFormerV2-B     | 34.92     | 0.99     |
| **ConvIR + UDP (Ours)** | 34.82     | 0.99     |
| **FSNet + UDP (Ours)**  | **35.31** | **0.99** |

</details>

🌙 Nighttime Dehazing
<details> <summary>Table 3. GTA5</summary>

| Method                   | PSNR      | SSIM      |
| ------------------------ | --------- | --------- |
| GS                       | 21.02     | 0.639     |
| MRP                      | 20.92     | 0.646     |
| Ancuti et al.            | 20.59     | 0.623     |
| Yan et al.               | 27.00     | 0.850     |
| CycleGAN                 | 21.75     | 0.696     |
| Jin et al.               | 30.38     | 0.904     |
| ConvIR-B **(Baseline)**  | 31.83     | 0.921     |
| PoolNet-B **(Baseline)** | 31.53     | 0.921     |
| PoolNet + UDP            | 32.78     | 0.930     |
| **ConvIR + UDP**         | **33.12** | **0.933** |

</details>

<details> <summary>Table 4. NHR</summary>

| Method                  | PSNR      | SSIM      |
| ----------------------- | --------- | --------- |
| NDIM                    | 14.31     | 0.526     |
| GS                      | 17.32     | 0.629     |
| MRPF                    | 16.95     | 0.667     |
| MRP                     | 19.93     | 0.777     |
| OSFD                    | 21.32     | 0.804     |
| HCD                     | 23.43     | 0.953     |
| FocalNet                | 25.35     | 0.969     |
| Jin et al.              | 26.56     | 0.890     |
| FSNet **(Baseline)**    | 26.30     | 0.976     |
| ConvIR-B **(Baseline)** | 29.49     | 0.983     |
| PoolNet-B               | 28.28     | 0.980     |
| FSNet + UDP             | 28.09     | 0.980     |
| **ConvIR + UDP**        | **29.54** | **0.983** |

</details>

🏞️Real-World Image Dehazing

<details> <summary>Table 5. Dense-Haze & NH-HAZE</summary>

| Method                  | Dense PSNR | Dense SSIM | Dense LPIPS | NH PSNR   | NH SSIM  | NH LPIPS   |
| ----------------------- | ---------- | ---------- | ----------- | --------- | -------- | ---------- |
| DehazeNet               | 13.84      | 0.43       | -           | 16.62     | 0.52     | -          |
| MSBDN                   | 15.37      | 0.49       | -           | 19.23     | 0.71     | -          |
| DeHamer                 | 16.62      | 0.56       | 0.6346      | 20.66     | 0.68     | 0.3837     |
| PMNet                   | 16.79      | 0.51       | -           | 20.42     | 0.73     | -          |
| MB-TaylorFormer-B       | 16.66      | 0.56       | 0.6125      | -         | -        | -          |
| C²PNet                  | 16.88      | 0.57       | -           | 20.24     | 0.69     | -          |
| FocalNet                | 17.07      | 0.63       | 0.6087      | 20.43     | 0.79     | 0.3780     |
| SFNet                   | 17.46      | 0.58       | **0.5689**  | 20.46     | 0.80     | -          |
| FSNet **(Baseline)**    | 17.13      | 0.65       | 0.5756      | 20.55     | 0.81     | 0.3624     |
| ConvIR-S **(Baseline)** | 17.45      | 0.65       | 0.6000      | 20.65     | 0.80     | 0.3669     |
| **ConvIR + UDP**        | 17.55      | **0.67**   | 0.5813      | **20.98** | **0.82** | **0.3567** |
| **FSNet + UDP**         | **17.85**  | 0.65       | 0.6033      | 20.94     | 0.82     | 0.3732     |

</details>

🌍Remote Sensing Image Dehazing
<details> <summary>Table 6. Remote Sensing Dehazing</summary>

| Method                   | Thin PSNR | Thin SSIM | Moderate PSNR | Moderate SSIM | Thick PSNR | Thick SSIM |
| ------------------------ | --------- | --------- | ------------- | ------------- | ---------- | ---------- |
| AOD-Net                  | 19.54     | 0.854     | 20.10         | 0.885         | 15.92      | 0.731      |
| H2RL-Net                 | 20.91     | 0.880     | 22.34         | 0.906         | 17.41      | 0.768      |
| FCFT-Net                 | 23.59     | 0.913     | 22.88         | 0.927         | 20.03      | 0.816      |
| C²PNet                   | 19.62     | 0.880     | 24.79         | 0.940         | 16.83      | 0.790      |
| Restormer                | 23.08     | 0.912     | 24.73         | 0.933         | 18.58      | 0.762      |
| Trinity-Net              | 21.55     | 0.884     | 23.35         | 0.895         | 20.97      | 0.823      |
| UMWTransformer           | 24.29     | 0.919     | 26.65         | 0.946         | 20.07      | 0.825      |
| FocalNet                 | 24.16     | 0.916     | 25.99         | 0.947         | 21.69      | 0.847      |
| ConvIR-S **(Baseline)**  | 25.11     | 0.978     | 26.79         | 0.978         | 22.65      | 0.950      |
| PoolNet-S **(Baseline)** | 25.02     | 0.979     | 27.02         | 0.979         | 22.73      | **0.955**  |
| **ConvIR + UDP**         | 25.48     | 0.979     | 28.07         | **0.981**     | 22.95      | 0.953      |
| **PoolNet + UDP**        | **26.20** | **0.980** | **28.26**     | 0.979         | **23.13**  | 0.951      |

</details>

🧪 All-in-One Image Restoration Benchmarks

<details> <summary>Table 7. Performance on Five Challenging Benchmarks</summary>

| Method                    | Dehaze    |           | Derain    |           | Denoise   |           | Deblur    |           | Low-Light |           | Average   |           |
| ------------------------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- |
|                           | PSNR      | SSIM      | PSNR      | SSIM      | PSNR      | SSIM      | PSNR      | SSIM      | PSNR      | SSIM      | PSNR      | SSIM      |
| DehazeFormer (TIP’23)     | 25.31     | 0.937     | 33.68     | 0.954     | 30.89     | 0.880     | 25.93     | 0.785     | 21.31     | 0.819     | 27.42     | 0.875     |
| Retinexformer (ICCV’23)   | 24.81     | 0.933     | 32.68     | 0.940     | 30.84     | 0.880     | 25.09     | 0.779     | 22.76     | 0.863     | 27.24     | 0.873     |
| SwinIR (ICCVW’21)         | 21.50     | 0.891     | 30.78     | 0.923     | 30.59     | 0.868     | 24.52     | 0.773     | 17.81     | 0.723     | 25.04     | 0.835     |
| Restormer (CVPR’22)       | 24.09     | 0.927     | 34.81     | 0.960     | 31.49     | 0.884     | 27.22     | 0.829     | 20.41     | 0.806     | 27.60     | 0.881     |
| FSNet (TPAMI’23)          | 25.53     | 0.943     | 36.07     | 0.968     | 31.33     | 0.883     | 28.32     | 0.869     | 22.29     | 0.829     | 28.71     | 0.898     |
| TransWeather (CVPR’22)    | 21.32     | 0.885     | 29.43     | 0.905     | 29.00     | 0.841     | 25.12     | 0.757     | 21.21     | 0.792     | 25.22     | 0.836     |
| AirNet (CVPR’22)          | 21.04     | 0.884     | 32.98     | 0.951     | 30.91     | 0.882     | 24.35     | 0.781     | 18.18     | 0.735     | 25.49     | 0.846     |
| PromptIR **(Baseline)**   | 26.54     | 0.949     | 36.37     | 0.970     | **31.47** | 0.886     | 28.71     | 0.881     | 22.68     | 0.832     | 29.15     | 0.904     |
| AdaIR **(Baseline)**      | 30.53     | 0.978     | *38.02*   | *0.981*   | 31.35     | *0.889*   | 28.12     | 0.858     | 23.00     | 0.845     | 30.20     | 0.910     |
| DCPT-PromptIR             | 30.72     | 0.977     | 37.32     | 0.978     | 31.32     | 0.885     | *28.84*   | *0.877*   | *23.35*   | 0.840     | 30.31     | 0.911     |
| DA-RCOT                   | 30.96     | 0.975     | 37.87     | 0.980     | 31.23     | 0.888     | 28.68     | 0.872     | 23.25     | 0.836     | *30.40*   | 0.911     |
| Perceive-IR               | 28.19     | 0.964     | 37.25     | 0.977     | *31.44*   | 0.887     | **29.46** | **0.886** | 22.88     | 0.833     | 29.84     | 0.909     |
| Pool-AIO                  | 30.25     | 0.977     | 37.85     | 0.981     | 31.24     | 0.887     | 27.66     | 0.844     | 22.66     | 0.841     | 29.93     | 0.906     |
| DPPD-PromptIR             | 30.31     | 0.980     | 37.32     | 0.980     | 31.33     | 0.885     | 28.74     | 0.875     | 22.73     | 0.846     | 30.09     | *0.913*   |
| VLU-Net                   | 30.84     | 0.980     | **38.54** | **0.982** | 31.43     | **0.891** | 27.46     | 0.840     | 22.29     | 0.833     | 30.11     | 0.905     |
| **PromptIR + UDP (Ours)** | *31.33*   | *0.980*   | 37.63     | 0.980     | 31.25     | 0.883     | 28.34     | 0.868     | 23.18     | *0.851*   | 30.35     | 0.912     |
| **AdaIR + UDP (Ours)**    | **31.41** | **0.980** | 37.85     | 0.980     | 31.28     | 0.888     | 28.62     | 0.870     | **23.53** | **0.854** | **30.55** | **0.915** |

</details>

## Citation

If you find our repo useful for your research, please consider citing our paper:

```bibtex
@article{zuo2026udpnet,
  title={{UDPNet}: Unleashing Depth-based Priors for Robust Image Dehazing},
  author={Zuo, Zengyuan and Jiang, Junjun and Wu, Gang and Liu, Xianming},
  journal={arXiv preprint arXiv:2601.06909},
  year={2026}
}
```

## Acknowledgments

This code is based on [FSNet](https://github.com/c-yn/FSNet), [ConvIR](https://github.com/c-yn/ConvIR), [PoolNet](https://github.com/c-yn/PoolNet) and [AdaIR](https://github.com/c-yn/AdaIR).

## Contact
📮📮📮 Should you have any problem, please contact Zengyuan Zuo<3565741165@qq.com>. We will response to your request as soon as possible!

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