# Perceptual Multi-Exposure Fusion

arXiv: https://arxiv.org/abs/2210.09604 · HTML: https://mlsanity.com/paper/2210.09604/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 61 references that are in the map.
- First posted on arXiv 18 Oct 2022.

## Builds on (most cited first, 3 of 3)

- [DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs](https://mlsanity.com/paper/1712.07384/) (2017)
- [Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data](https://mlsanity.com/paper/1904.08632/) (2019)
- [Scene Segmentation-Based Luminance Adjustment for Multi-Exposure Image Fusion](https://mlsanity.com/paper/1903.07428/) (2019)

## Built on by (most cited first, 3 of 3)

- [UNICE: Training A Universal Image Contrast Enhancer](https://mlsanity.com/paper/2507.17157/) (2025)
- [AutoLumNet: Monotone Optimal Transport for Single-Shot Exposure Correction](https://mlsanity.com/paper/2608.19860/) (2026)
- [Pixel-wise Exposure for Highly Robust In-Vehicle Remote-PPG](https://mlsanity.com/paper/2609.36607/) (2026)

## Abstract (arXiv)

As an ever-increasing demand for high dynamic range (HDR) scene shooting, multi-exposure image fusion (MEF) technology has abounded. In recent years, multi-scale exposure fusion approaches based on detail-enhancement have led the way for improvement in highlight and shadow details. Most of such methods, however, are too computationally expensive to be deployed on mobile devices. This paper presents a perceptual multi-exposure fusion method that not just ensures fine shadow/highlight details but with lower complexity than detailenhanced methods. We analyze the potential defects of three classical exposure measures in lieu of using detail-enhancement component and improve two of them, namely adaptive Wellexposedness (AWE) and the gradient of color images (3-D gradient). AWE designed in YCbCr color space considers the difference between varying exposure images. 3-D gradient is employed to extract fine details. We build a large-scale multiexposure benchmark dataset suitable for static scenes, which contains 167 image sequences all told. Experiments on the constructed dataset demonstrate that the proposed method exceeds existing eight state-of-the-art approaches in terms of visually and MEF-SSIM value. Moreover, our approach can achieve a better improvement for current image enhancement techniques, ensuring fine detail in bright light.