From 37cd967020bac076daf8825569753909b60add5e Mon Sep 17 00:00:00 2001 From: Zhaozixiang1228 <44187438+Zhaozixiang1228@users.noreply.github.com> Date: Sat, 8 Apr 2023 20:16:52 +0200 Subject: [PATCH] main --- README.md | 136 ++++++++++++++++++++++++++++++++++++- image/IVF1.png | Bin 0 -> 888997 bytes image/IVF2.png | Bin 0 -> 1032662 bytes image/MIF.png | Bin 0 -> 1168188 bytes image/MMDet.png | Bin 0 -> 125152 bytes image/MMSeg.png | Bin 0 -> 129513 bytes image/Quantitative_IVF.png | Bin 0 -> 387569 bytes image/Quantitative_MIF.png | Bin 0 -> 427142 bytes image/Workflow.png | Bin 0 -> 255302 bytes 9 files changed, 135 insertions(+), 1 deletion(-) create mode 100644 image/IVF1.png create mode 100644 image/IVF2.png create mode 100644 image/MIF.png create mode 100644 image/MMDet.png create mode 100644 image/MMSeg.png create mode 100644 image/Quantitative_IVF.png create mode 100644 image/Quantitative_MIF.png create mode 100644 image/Workflow.png diff --git a/README.md b/README.md index 30c7642..b2933b8 100644 --- a/README.md +++ b/README.md @@ -1 +1,135 @@ -# MMIF-CDDFuse \ No newline at end of file +# MMIF-CDDFuse +Codes for ***CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion. (CVPR 2023)*** + +[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). + +-[*[Paper]*]() +-[*[ArXiv]*](https://arxiv.org/abs/2104.06977) +-[*[Supplementary Materials]*]() + + +## Citation + + + + +``` +@article{DBLP:journals/corr/abs-2211-14461, + 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}, + title = {CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion}, + booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, + month = {}, + year = {2023}, + pages = {} +} +``` + +## Abstract + +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. + +## Usage + +### Network Architecture + +Our CDDFuse is implemented in ``net.py``. + +### Usage + +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. + +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. + +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. + +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. + +The testing results will be printed in the terminal. + +The output for ``'test_IVF.py'`` is: + +``` +================================================================================ +The test result of TNO : + EN SD SF MI SCD VIF Qabf SSIM +CDDFuse 7.12 46.0 13.15 2.19 1.76 0.77 0.54 1.03 +================================================================================ + +================================================================================ +The test result of RoadScene : + EN SD SF MI SCD VIF Qabf SSIM +CDDFuse 7.44 54.67 16.36 2.3 1.81 0.69 0.52 0.98 +================================================================================ +``` +which can match the results in Table 1 in our original paper. + +The output for ``'test_IVF.py'`` is: + +``` +================================================================================ +The test result of MRI_CT : + EN SD SF MI SCD VIF Qabf SSIM +CDDFuse_IVF 4.83 88.59 33.83 2.24 1.74 0.5 0.59 1.31 +CDDFuse_MIF 4.88 79.17 38.14 2.61 1.41 0.61 0.68 1.34 +================================================================================ + +================================================================================ +The test result of MRI_PET : + EN SD SF MI SCD VIF Qabf SSIM +CDDFuse_IVF 4.23 81.68 28.04 1.87 1.82 0.66 0.65 1.46 +CDDFuse_MIF 4.22 70.73 29.57 2.03 1.69 0.71 0.71 1.49 +================================================================================ + +================================================================================ +The test result of MRI_SPECT : + EN SD SF MI SCD VIF Qabf SSIM +CDDFuse_IVF 3.91 71.81 20.66 1.9 1.87 0.65 0.68 1.45 +CDDFuse_MIF 3.9 58.31 20.87 2.49 1.35 0.97 0.78 1.48 +================================================================================ +``` + +which can match the results in Table 5 in our original paper. + +## CDDFuse + +### Illustration of our CDDFuse model. + + + +### Qualitative fusion results. + + + + + + + +### Quantitative fusion results. + +Infrared-Visible Image Fusion + + + +Medical Image Fusion + + + +MM detection + + + +MM segmentation + + + + +## Related Work + +- 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. + +- 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. + +- 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. + +- 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. + diff --git a/image/IVF1.png b/image/IVF1.png new file mode 100644 index 0000000000000000000000000000000000000000..8267a8a65b7ae6e3df08c7671a97286133983c6a GIT binary patch literal 888997 zcmZs>1yodD_%=Fph``V#3?m{jbaxFQh=g>9lr%_p$&iY4D=jc|H&T)#AcJ%ZICM96 ze&4-!-S7Xcd)7MZtP^|hv)}#fH=g&6(R`!)l#q@P002CFr2^Fk0Psiv0E|+6O!Sk9 z=1E@bvXc__S2I)OB?`zkk3IuLGzV@0!Y)t)z-nu1^`fr$xZXoc{@iHjp!8V z)ciskoAS%iUK>;8X{SmOHwPa9scO$>09Y8R;|A4AD#7PxTMaE#NU8Ew!n3IB#@oL?{5^8{iXc#&yCB6i9jDM>r(Ik9eYw7K|l zBSDO;D?Z7BmU=lhdMYsj$X 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