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.

Abstract

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.

Prerequisites

✔️ MATLAB 2015+

Usage

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

Results

Weights which are extracted via PAS-MEF:

Fusion results of MDO-MEF and PAS-MEF:

Citing this work

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.

Related Research Projects

• Image Fusion Through Linear Embeddings IEEE-ICIP-21: A image fusion technique based on linear embeddings (IEEE ICIP 21).

• 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).