Multi-Grained Text-Guided Image Fusion for Multi-Exposure and Multi-Focus Scenarios

• URL: http://arxiv.org/abs/2512.20556v1
• Date: Tue, 23 Dec 2025 17:55:35 GMT
• Title: Multi-Grained Text-Guided Image Fusion for Multi-Exposure and Multi-Focus Scenarios
• Authors: Mingwei Tang, Jiahao Nie, Guang Yang, Ziqing Cui, Jie Li,
• Abstract summary: Multi-grained Text-guided Image Fusion (MTIF) is a novel fusion paradigm with three key designs.<n>First, it introduces multi-grained textual descriptions that separately capture fine details, structural cues, and semantic content.<n>Second, it involves supervision signals at each to facilitate alignment between visual and textual features.<n>Third, it adopts a saliency-driven enrichment module to augment training data with dense semantic content.
• Score: 12.461120447513487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image fusion aims to synthesize a single high-quality image from a pair of inputs captured under challenging conditions, such as differing exposure levels or focal depths. A core challenge lies in effectively handling disparities in dynamic range and focus depth between the inputs. With the advent of vision-language models, recent methods incorporate textual descriptions as auxiliary guidance to enhance fusion quality. However, simply incorporating coarse-grained descriptions hampers the understanding of fine-grained details and poses challenges for precise cross-modal alignment. To address these limitations, we propose Multi-grained Text-guided Image Fusion (MTIF), a novel fusion paradigm with three key designs. First, it introduces multi-grained textual descriptions that separately capture fine details, structural cues, and semantic content, guiding image fusion through a hierarchical cross-modal modulation module. Second, it involves supervision signals at each granularity to facilitate alignment between visual and textual features and enhance the utility of auxiliary text. Third, it adopts a saliency-driven enrichment module to augment training data with dense semantic content, further strengthening the cross-modal modulation and alignment. Extensive experiments show that MTIF consistently outperforms previous methods on both multi-exposure and multi-focus image fusion tasks.

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