PAS-MEF: Multi-exposure image fusion based on principal component analysis, adaptive well-exposedness and saliency map (IEEE ICASSP 2022)
This is the implementation for PAS-MEF: Multi-exposure image fusion based on principal component analysis, adaptive well-exposedness and saliency map (IEEE ICASSP 2022), Diclehan Karakaya, Oguzhan Ulucan, Mehmet Turkan, arXiv preprint arXiv:2105.11809, 2021.
High dynamic range (HDR) imaging enables to immortalize natural scenes similar to the way that they are perceived by human observers. With regular low dynamic range (LDR) capture/display devices, significant details may not be preserved in images due to the huge dynamic range of natural scenes. To minimize the information loss and produce high quality HDR-like images for LDR screens, this study proposes an efficient multi-exposure fusion (MEF) approach with a simple yet effective weight extraction method relying on principal component analysis, adaptive well-exposedness and saliency maps. These weight maps are later refined through a guided filter and the fusion is carried out by employing a pyramidal decomposition. Experimental comparisons with existing techniques demonstrate that the proposed method produces very strong statistical and visual results.
✔️ MATLAB 2015+
Required Input : Source static image sequence in RGB.
Output:
(1) Fused : The fused image.
(2) Weights : PAS-MEF Weights
(3) run-time : Computational Complexity of the PAS-MEF in seconds
(4) MEF_SSIM : Statistical result of the image according to:
Perceptual Quality Assessment for Multi-Exposure Image Fusion,
Kede Ma, Kai Zeng, Zhou Wang,
IEEE Transactions on Image Processing, vol. 24,pp. 3345 - 3356, Nov.2015.
Usage:
(1) Install the PAS-MEF package by using the "run install" command
(2) Select the image stacks folder to be fused
(3) Run the PAS-MEF code in order to obtain results
(4) Uninstall the PAS-MEF packages by using the "run uninstall" command
Weights which are extracted via PAS-MEF:
Fusion results of MDO-MEF and PAS-MEF:
If you find this work useful in your research, please consider citing:
@inproceedings{karakaya2022pas,
title={PAS-MEF: Multi-exposure image fusion based on principal component analysis, adaptive well-exposedness and saliency map},
author={Karakaya, Diclehan and Ulucan, Oguzhan and Turkan, Mehmet},
booktitle={ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={2345--2349},
year={2022},
organization={IEEE}
}
- Karakaya, D., Ulucan, O., & Turkan, M. (2021). PAS-MEF: Multi-exposure image fusion based on principal component analysis, adaptive well-exposedness and saliency map. arXiv preprint arXiv:2105.11809.
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Image Fusion Through Linear Embeddings IEEE-ICIP-21: A image fusion technique based on linear embeddings (IEEE ICIP 21).
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Multi-Exposure Image Fusion based on Linear Embeddings and Watershed Masking: A multi-exposure image fusion technique based on linear embeddings & Watershed Masking (Elsevier Signal Processing).