# Scene Segmentation-Based Luminance Adjustment for Multi-Exposure Image Fusion

arXiv: https://arxiv.org/abs/1903.07428 · HTML: https://mlsanity.com/paper/1903.07428/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2019.

## Built on by (most cited first, 15 of 15)

- [GTA-HDR: A Large-Scale Synthetic Dataset for HDR Image Reconstruction](https://mlsanity.com/paper/2403.17837/) (2024)
- [Perceptual Region-Driven Infrared-Visible Co-Fusion for Extreme Scene Enhancement](https://mlsanity.com/paper/2512.06400/) (2025)
- [Multi-color balance for color constancy](https://mlsanity.com/paper/2105.10228/) (2021)
- [Perceptual Multi-Exposure Fusion](https://mlsanity.com/paper/2210.09604/) (2022)
- [A Dual Domain Multi-exposure Image Fusion Network based on the Spatial-Frequency Integration](https://mlsanity.com/paper/2312.10604/) (2023)
- [Separated-Spectral-Distribution Estimation Based on Bayesian Inference with Single RGB Camera](https://mlsanity.com/paper/2106.01861/) (2021)
- [Spatially varying white balancing for mixed and non-uniform illuminants](https://mlsanity.com/paper/2109.01350/) (2021)
- [Self-Supervised Intrinsic Image Decomposition Network Considering Reflectance Consistency](https://mlsanity.com/paper/2111.04506/) (2021)
- [Reflectance-Guided, Contrast-Accumulated Histogram Equalization](https://mlsanity.com/paper/2209.06405/) (2022)
- [Supervised Image Segmentation for High Dynamic Range Imaging](https://mlsanity.com/paper/2212.03002/) (2022)
- [High Dynamic Range Imaging via Visual Attention Modules](https://mlsanity.com/paper/2307.14705/) (2023)
- [Scene-Segmentation-Based Exposure Compensation for Tone Mapping of High Dynamic Range Scenes](https://mlsanity.com/paper/2410.19839/) (2024)
- [UNICE: Training A Universal Image Contrast Enhancer](https://mlsanity.com/paper/2507.17157/) (2025)
- [A Hardware-Algorithm Co-Designed Framework for HDR Imaging and Dehazing in Extreme Rocket Launch Environments](https://mlsanity.com/paper/2601.08162/) (2026)
- [Synergistic Event-SVE Imaging for Quantitative Propellant Combustion Diagnostics](https://mlsanity.com/paper/2603.25054/) (2026)

## Abstract (arXiv)

We propose a novel method for adjusting luminance for multi-exposure image fusion. For the adjustment, two novel scene segmentation approaches based on luminance distribution are also proposed. Multi-exposure image fusion is a method for producing images that are expected to be more informative and perceptually appealing than any of the input ones, by directly fusing photos taken with different exposures. However, existing fusion methods often produce unclear fused images when input images do not have a sufficient number of different exposure levels. In this paper, we point out that adjusting the luminance of input images makes it possible to improve the quality of the final fused images. This insight is the basis of the proposed method. The proposed method enables us to produce high-quality images, even when undesirable inputs are given. Visual comparison results show that the proposed method can produce images that clearly represent a whole scene. In addition, multi-exposure image fusion with the proposed method outperforms state-of-the-art fusion methods in terms of MEF-SSIM, discrete entropy, tone mapped image quality index, and statistical naturalness.