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Official Implementation of MEF-Net
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Deep Guided Learning for Fast Multi-Exposure Image Fusion

This is the implementation for Deep Guided Learning for Fast Multi-Exposure Image Fusion, Kede Ma, Zhengfang Duanmu, Hanwei Zhu, Yuming Fang, Zhou Wang, IEEE Transactions on Image Processing, to appear, 2019.


We propose a fast multi-exposure image fusion (MEF) method, namely MEF-Net, for static image sequences of arbitrary spatial resolution and exposure number. We first feed a low-resolution version of the input sequence to a fully convolutional network for weight map prediction. We then jointly upsample the weight maps using a guided filter. The final image is computed by a weighted fusion. Unlike conventional MEF methods, MEF-Net is trained end-to-end by optimizing the perceptually calibrated MEF structural similarity (MEF-SSIM) index over a database of training sequences at full resolution. Across an independent set of test sequences, we find that the optimized MEF-Net achieves consistent improvement in visual quality for most sequences, and runs 10 to 1000 times faster than state-of-the-art methods.

MEF-Net Framework



The release version of MEF-Net was implemented and has been tested in Ubuntu 16.04 with

  • Python = 3.6.2
  • pytorch = 0.4.1
  • torchvision = 0.2.1


Please refer to the respective papers mentioned in the manuscript. We do not directly provide the large-scale dataset for MEF-Net. We collect more than 1000 exposure sequence, and screening the local and non-aligned global motion sequences. Here, we provide only one exposure sequence "Corridor" to test training code.


We recommend to use GPU to train the method:

python --train True --use_cuda True


Run the following command to test the exposure sequence "Corridor" with the default settings in MEF-Net on GPU-mode:

python --train False --use_cuda True --ckpt

on CPU-mode:

python --train False --use_cuda False --ckpt


  • K. Ma, K. Zeng, and Z. Wang, “Perceptual quality assessment for multi-exposure image fusion,” IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 3345–3356, Nov. 2015.
  • K. Ma, Z. Duanmu, H. Yeganeh, and Z. Wang, “Multi-exposure image fusion by optimizing a structural similarity index,” IEEE Transactions on Computational Imaging, vol. 4, no. 1, pp. 60–72, Mar. 2018.
  • H. Wu, S. Zheng, J. Zhang, and K. Huang, “Fast end-to-end trainable guided filter,” in IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 1838–1847.


title={Deep Guided Learning for Fast Multi-Exposure Image Fusion},
author={Kede, Ma and Zhengfang, Duanmu and Hanwei, Zhu and Yuming, Fang and Zhou, Wang},
journal={IEEE Transactions on Image Processing},
year={to appear, 2019}


The authors would like to thank Huikai Wu for his implementation of Fast End-to-End Trainable Guided Filter in Pytorch.

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