PAS-MEF: Image Fusion for Dynamic and Static Scenes

Source code of Ghosting-free multi-exposure image fusion for static and dynamic scenes (Elsevier's Signal Processing)

Ghosting-free-multi-exposure-image-fusion-for-static-and-dynamic-scenes

This is the source code of Ghosting-free multi-exposure image fusion for static and dynamic scenes (Elsevier's Signal Processing), Oguzhan Ulucan, Diclehan Ulucan, Mehmet Turkan.

Abstract

The visual system enables humans to perceive all details of the real-world with vivid colors, while high dynamic range (HDR) technology aims at capturing natural scenes in a closer way to human perception through a large dynamic range of color gamut. Especially for traditional –low dynamic range (LDR)– devices, HDR-like image generation is an attractive research topic. Blending a stack of input LDR exposures is called multi-exposure image fusion (MEF). MEF is indeed a very challenging problem and it is highly prone to halo effects or ghosting and motion blur in the cases when there are spatial discontinuities in between input exposures. To overcome these artifacts, MEF keeps the “best” quality regions of each exposure via a weight characterization scheme. This paper proposes an effective weight map extraction framework which relies on principal component analysis, adaptive well-exposedness and saliency maps. The characterized maps are later refined by a guided filter and a blended output image is obtained via pyramidal decomposition. Comprehensive experiments and comparisons demonstrate that the developed algorithm generates very strong statistical and visual results for both static and dynamic scenes. In addition, the designed method is successfully applied to the visible-infrared image fusion problem without any further optimization.

Citing this work

If you find this work useful in your research, please cite it as follows;

@article{ulucan2022ghosting,
  title={Ghosting-Free Multi-Exposure Image Fusion for Static and Dynamic Scenes},
  author={Ulucan, Oguzhan and Ulucan, Diclehan and Turkan, Mehmet},
  journal={Signal Processing},
  pages={108774},
  year={2022},
  publisher={Elsevier}
 }

• Ulucan, O., Ulucan, D., & Turkan, M. (2022). Ghosting-Free Multi-Exposure Image Fusion for Static and Dynamic Scenes. Signal Processing, 108774.

Related Research Projects

• PAS-MEF: Multi-exposure image fusion based on principal component analysis, adaptive well-exposedness and saliency map: Initial version of PAS-MEF (IEEE ICASSP 22).

• 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).