---
title: "TextMEF: Text-guided Prompt Learning for Multi-exposure Image Fusion"
authors:
  - "Jinyuan Liu"
  - "Qianjun Huang"
  - "Guanyao Wu"
  - "Di Wang"
  - "Zhiying Jiang"
  - "Long Ma"
  - "Risheng Liu"
  - "Xin Fan"
doi: "10.24963/ijcai.2025/175"
url: "https://ijcai.org/proceedings/2025/175"
pdf: "https://ijcai.org/proceedings/2025/0175.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "1567-1575"
track: "Main Track"
keywords:
  - "Computer Vision: CV: Low-level Vision"
---

# TextMEF: Text-guided Prompt Learning for Multi-exposure Image Fusion

Jinyuan Liu, Qianjun Huang, Guanyao Wu, Di Wang, Zhiying Jiang, Long Ma, Risheng Liu, Xin Fan

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 1567-1575. DOI: [10.24963/ijcai.2025/175](https://doi.org/10.24963/ijcai.2025/175)

## Abstract

Multi-exposure image fusion~(MEF) aims to integrate a set of low dynamic range images, producing a single image with a higher dynamic range than either one. Despite significant advancements, current MEF approaches still struggle to handle extremely over- or under-exposed conditions, resulting in unsatisfactory visual effects such as hallucinated details and distorted color tones. With this regard, we propose TextMEF, a prompt-driven fusion method enhanced by prompt learning, for multi-exposure image fusion. Specifically, we learn a set of prompts based on text-image similarity among negative and positive samples (over-exposed, under-exposed images, and well-exposed ones). These learned prompts are seamlessly integrated into the loss function, providing high-level guidance for constraining non-uniform exposure regions. Furthermore, we develop a attention Mamba module effectively translates over-/under- exposed regional features into exposure invariant space and ensure them to build efficient long-range dependency to high dynamic range image. Extensive experimental results on three publicly available benchmarks demonstrate that our TextMEF significantly outperforms state-of-the-art approaches in both visual inspection and objective analysis.