# PAS-MEF: Multi-exposure image fusion based on principal component analysis, adaptive well-exposedness and saliency map

arXiv: https://arxiv.org/abs/2105.11809 · HTML: https://mlsanity.com/paper/2105.11809/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 20 references that are in the map.
- First posted on arXiv 25 May 2021.

## Built on by (most cited first, 2 of 2)

- [Entropy-Driven Genetic Optimization for Deep-Feature-Guided Low-Light Image Enhancement](https://mlsanity.com/paper/2505.11246/) (2025)
- [Synergistic Event-SVE Imaging for Quantitative Propellant Combustion Diagnostics](https://mlsanity.com/paper/2603.25054/) (2026)

## Abstract (arXiv)

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.