---
title: "MLLM-ITM: Multimodal Large Language Model Promotes Inverse Tone Mapping"
authors:
  - "Jingchao Peng"
  - "Thomas Bashford-Rogers"
  - "Haitao Zhao"
  - "Kurt Debattista"
doi: "10.24963/ijcai.2026/169"
url: "https://ijcai.org/proceedings/2026/169"
pdf: "https://ijcai.org/proceedings/2026/0169.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "1514-1522"
track: "Main Track"
keywords:
  - "Computer Vision: Machine learning for vision"
  - "Computer Vision: Image and video synthesis and generation"
  - "Computer Vision: Multimodal learning"
  - "Computer Vision: Low-level Vision"
---

# MLLM-ITM: Multimodal Large Language Model Promotes Inverse Tone Mapping

Jingchao Peng, Thomas Bashford-Rogers, Haitao Zhao, Kurt Debattista

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 1514-1522. DOI: [10.24963/ijcai.2026/169](https://doi.org/10.24963/ijcai.2026/169)

## Abstract

High dynamic range (HDR) imaging is crucial for capturing real-world lighting conditions.
HDR imaging is traditionally achieved either by fusing multiple exposure frames or via inverse tone mapping from a single SDR image.
However, the multi-exposure HDR method is prone to motion-induced artefacts and imposes demanding hardware requirements, limiting its practical applicability. 
Traditional inverse tone-mapping techniques primarily rely on pixel-wise regression methods, which ignore semantic scene contexts and thus frequently introduce halo artefacts and structural distortions.
To address this limitation, this paper proposes MLLM-ITM, a novel inverse tone-mapping framework incorporating multimodal large language models (MLLMs). 
MLLM-ITM utilizes cross-modal features extracted from a frozen MLLM to simultaneously encode visual features and semantic understanding. These features are integrated into a downstream HDR reconstruction backbone through lightweight adapters, enabling content-aware dynamic-range expansion.
Furthermore, the decoupled design between MLLM and HDR backbone avoids costly fine-tuning of MLLMs and remains model-agnostic, allowing effortless substitution with emerging multimodal architectures. Extensive experiments on public benchmarks demonstrate that the proposed MLLM-ITM achieves state-of-the-art performance compared with existing inverse tone-mapping methods, highlighting the effectiveness of cross-modal semantic priors in enhancing HDR imaging performance.