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

## TL;DR

This paper introduces a new luminance adjustment technique based on scene segmentation to enhance multi-exposure image fusion, resulting in clearer, more informative images even with suboptimal input exposures.

## Contribution

It proposes two novel scene segmentation methods based on luminance distribution and demonstrates their effectiveness in improving fusion quality over existing methods.

## Key findings

- Improved fused image clarity and scene representation.
- Outperforms state-of-the-art methods in multiple quality metrics.
- Enables high-quality fusion with less ideal input images.

## Abstract

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.