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Deep Guided Learning for Fast Multi-Exposure Image Fusion

framework

Introduction

This repository contains reference code for the paper Deep Guided Learning for Fast Multi-Exposure Image Fusion, Kede Ma, Zhengfang Duanmu, Hanwei Zhu, Yuming Fang, Zhou Wang, IEEE Transactions on Image Processing, vol. 29, pp. 2808-2819, 2020.

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.

Prerequisites

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

  • Python = 3.6.2
  • PyTorch = 0.4.1
  • torchvision = 0.2.1

Dataset

We do not have the right to distribute the large-scale dataset used for training and testing MEF-Net. Please kindly refer to the respective authors acknowledged in the manuscript.

Train

We provide only one exposure sequence "Corridor" to test the training code. We recommend to use GPU:

python Main.py --train True --use_cuda True

Test

To test the exposure sequence "Corridor" with the default settings of MEF-Net on GPU-mode:

python Main.py --train False --use_cuda True --ckpt MEFNet_release.pt

on CPU-mode:

python Main.py --train False --use_cuda False --ckpt MEFNet_release.pt

Reference

  • 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.

Citation

@article{ma2019mefnet,
title={Deep Guided Learning for Fast Multi-Exposure Image Fusion},
author={Ma, Kede and Duanmu, Zhengfang and Zhu, Hanwei and Fang, Yuming and Wang, Zhou},
journal={IEEE Transactions on Image Processing},
volume={29},
number={},
pages={2808-2819},
year={2020}
}

Acknowledgment

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