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IID-MEF

Code of paper IID-MEF: A Multi-exposure Fusion Network Based on Intrinsic Image Decomposition.

Recommended Environment:

python = 2.7
tensorflow-gpu = 1.9.0
numpy = 1.15.4
scipy = 1.2.0

Prepare data :

Put multi-exposed images in the "dataset/train/..." for training
Put multi-exposed images in the "dataset/test/demo/..." for testing

Training :

Run "CUDA_VISIBLE_DEVICES=X python IID_Net_train.py" to implement the intrinsic image decomposition
Run "CUDA_VISIBLE_DEVICES=X python R/S/CFus_Net_train.py" to fuse the components.

To produce the HDR images :

Run "CUDA_VISIBLE_DEVICES=X python evaluate_Fus.py" to implement MEF, obtaining HDR images.

You can visualize the decomposed components by using our IIDNet:

Run "CUDA_VISIBLE_DEVICES=X python evaluate_IID.py" to perform the decomposition.

If this work is helpful to you, please cite it as：

@article{zhang2023iid,
  title={IID-MEF: A multi-exposure fusion network based on intrinsic image decomposition},
  author={Zhang, Hao and Ma, Jiayi},
  journal={Information Fusion},
  volume={95},
  pages={326--340},
  year={2023},
  publisher={Elsevier}
}

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iid-mef's Issues

Sequence–image fusion

Hello!
IID-MEF is an excellent work!
But I have a little question about the sequence image fusion.
The paper claims that IID-MEF can directly fuse three images. But the RFusNet, CFusNet, and SFusNet only have two inputs. So how to achieve sequence image fusion directly?
Thank you!

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