136 lines
7.1 KiB
Markdown
136 lines
7.1 KiB
Markdown
# MMIF-CDDFuse
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Codes for ***CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion. (CVPR 2023)***
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[Zixiang Zhao](https://zhaozixiang1228.github.io/), [Haowen Bai](), [Jiangshe Zhang](http://gr.xjtu.edu.cn/web/jszhang), [Yulun Zhang](https://yulunzhang.com/), [Shuang Xu](https://shuangxu96.github.io/), [Zudi Lin](https://zudi-lin.github.io/), [Radu Timofte](https://www.informatik.uni-wuerzburg.de/computervision/home/) and [Luc Van Gool](https://vision.ee.ethz.ch/people-details.OTAyMzM=.TGlzdC8zMjQ4LC0xOTcxNDY1MTc4.html).
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-[*[Paper]*]()
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-[*[ArXiv]*](https://arxiv.org/abs/2104.06977)
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-[*[Supplementary Materials]*]()
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## Citation
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```
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@article{DBLP:journals/corr/abs-2211-14461,
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author = {Zixiang Zhao and Haowen Bai and Jiangshe Zhang and Yulun Zhang and Shuang Xu and Zudi Lin and Radu Timofte and Luc Van Gool},
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title = {CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion},
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booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
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month = {},
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year = {2023},
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pages = {}
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}
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```
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## Abstract
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Multi-modality (MM) image fusion aims to render fused images that maintain the merits of different modalities, e.g., functional highlight and detailed textures. To tackle the challenge in modeling cross-modality features and decomposing desirable modality-specific and modality-shared features, we propose a novel Correlation-Driven feature Decomposition Fusion (CDDFuse) network. Firstly, CDDFuse uses Restormer blocks to extract cross-modality shallow features. We then introduce a dual-branch Transformer-CNN feature extractor with Lite Transformer (LT) blocks leveraging long-range attention to handle low-frequency global features and Invertible Neural Networks (INN) blocks focusing on extracting high-frequency local information. A correlation-driven loss is further proposed to make the low-frequency features correlated while the high-frequency features uncorrelated based on the embedded information. Then, the LT-based global fusion and INN-based local fusion layers output the fused image. Extensive experiments demonstrate that our CDDFuse achieves promising results in multiple fusion tasks, including infrared-visible image fusion and medical image fusion. We also show that CDDFuse can boost the performance in downstream infrared-visible semantic segmentation and object detection in a unified benchmark.
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## Usage
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### Network Architecture
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Our CDDFuse is implemented in ``net.py``.
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### Usage
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Pretrained models are available in ``'./models/CDDFuse_IVF.pth'`` and ``'./models/CDDFuse_MIF.pth'``, which are responsible for the Infrared-Visible Fusion (IVF) and Medical Image Fusion (MIF) tasks, respectively.
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The test datasets used in the paper have been stored in ``'./test_img/RoadScene'``, ``'./test_img/TNO'`` for IVF, ``'./test_img/MRI_CT'``, ``'./test_img/MRI_PET'`` and ``'./test_img/MRI_SPECT'`` for MIF.
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Unfortunately, since the size of **MSRS dataset** for IVF is 500+MB, we can not upload it for exhibition. The other datasets contain all the test images.
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If you want to infer with our CDDFuse and obtain the fusion results in our paper, please run ``'test_IVF.py'`` for IVF and ``'test_MIF.py'`` for MIF.
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The testing results will be printed in the terminal.
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The output for ``'test_IVF.py'`` is:
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```
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================================================================================
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The test result of TNO :
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EN SD SF MI SCD VIF Qabf SSIM
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CDDFuse 7.12 46.0 13.15 2.19 1.76 0.77 0.54 1.03
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================================================================================
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================================================================================
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The test result of RoadScene :
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EN SD SF MI SCD VIF Qabf SSIM
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CDDFuse 7.44 54.67 16.36 2.3 1.81 0.69 0.52 0.98
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================================================================================
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```
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which can match the results in Table 1 in our original paper.
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The output for ``'test_IVF.py'`` is:
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```
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================================================================================
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The test result of MRI_CT :
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EN SD SF MI SCD VIF Qabf SSIM
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CDDFuse_IVF 4.83 88.59 33.83 2.24 1.74 0.5 0.59 1.31
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CDDFuse_MIF 4.88 79.17 38.14 2.61 1.41 0.61 0.68 1.34
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================================================================================
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================================================================================
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The test result of MRI_PET :
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EN SD SF MI SCD VIF Qabf SSIM
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CDDFuse_IVF 4.23 81.68 28.04 1.87 1.82 0.66 0.65 1.46
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CDDFuse_MIF 4.22 70.73 29.57 2.03 1.69 0.71 0.71 1.49
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================================================================================
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================================================================================
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The test result of MRI_SPECT :
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EN SD SF MI SCD VIF Qabf SSIM
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CDDFuse_IVF 3.91 71.81 20.66 1.9 1.87 0.65 0.68 1.45
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CDDFuse_MIF 3.9 58.31 20.87 2.49 1.35 0.97 0.78 1.48
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================================================================================
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```
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which can match the results in Table 5 in our original paper.
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## CDDFuse
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### Illustration of our CDDFuse model.
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<img src="image//Workflow.png" width="90%" align=center />
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### Qualitative fusion results.
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<img src="image//IVF1.png" width="90%" align=center />
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<img src="image//IVF2.png" width="90%" align=center />
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<img src="image//MIF.png" width="90%" align=center />
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### Quantitative fusion results.
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Infrared-Visible Image Fusion
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<img src="image//Quantitative_IVF.png" width="60%" align=center />
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Medical Image Fusion
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<img src="image//Quantitative_MIF.png" width="60%" align=center />
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MM detection
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<img src="image//MMDet.png" width="60%" align=center />
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MM segmentation
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<img src="image//MMSeg.png" width="60%" align=center />
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## Related Work
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- Zixiang Zhao, Shuang Xu, Chunxia Zhang, Junmin Liu, Jiangshe Zhang and Pengfei Li, *DIDFuse: Deep Image Decomposition for Infrared and Visible Image Fusion.* IJCAI 2020: 970-976, https://www.ijcai.org/Proceedings/2020/135.
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- Zixiang Zhao, Shuang Xu, Jiangshe Zhang, Chengyang Liang, Chunxia Zhang and Junmin Liu, *Efficient and Model-Based Infrared and Visible Image Fusion via Algorithm Unrolling.* IEEE Transactions on Circuits and Systems for Video Technology, doi: 10.1109/TCSVT.2021.3075745, https://ieeexplore.ieee.org/document/9416456.
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- Zixiang Zhao, Jiangshe Zhang, Haowen Bai, Yicheng Wang, Yukun Cui, Lilun Deng, Kai Sun, Chunxia Zhang, Junmin Liu, Shuang Xu, *Deep Convolutional Sparse Coding Networks for Interpretable Image Fusion.* CVPR Workshop 2023.
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- Zixiang Zhao, Shuang Xu, Chunxia Zhang, Junmin Liu, Jiangshe Zhang, *Bayesian fusion for infrared and visible images, Signal Processing,* Volume 177, 2020, 107734, ISSN 0165-1684, https://doi.org/10.1016/j.sigpro.2020.107734.
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