Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2025

Abstract

Infrared-visible image fusion aims to integrate complementary information from two modalities to generate images with enriched semantic content. However, existing methods often neglect two critical aspects: the design of a local–global feature enhancement architecture and spatial alignment. To address these challenges, we propose Channel Selective and Spatial Alignment Fusion (CSSA-Fusion), a novel framework composed of two synergistic modules. The first is a selective channel and redundancy suppression module, which introduces a dual-branch selective channel attention mechanism to jointly capture local saliency and global channel importance for enhanced feature representation, and an informativeness–redundancy separation strategy to suppress redundant information while preserving discriminative features. The second is a directional feature processing module, consisting of a mechanism that decouples and recombines modality-specific and common representations to mitigate mutual interference, and a spatial alignment module that performs geometric alignment via horizontal and vertical coordinate decomposition to correct spatial discrepancies between modalities. Extensive experiments on benchmark datasets demonstrate that CSSA-Fusion consistently outperforms state-of-the-art deep learning methods on multiple quality metrics. The fused images exhibit superior visual quality with well-preserved textures and enhanced semantic details.

Keywords

Multimodal Imaging, Image Fusion, Image Quality, Deep Learning

Discipline

Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

MMAsia ’25: Proceedings of the 7th ACM International Conference on Multimedia in Asia, Kuala Lumpur, Malaysia, December 9-12

First Page

1

Last Page

7

ISBN

9798400720055

Identifier

10.1145/3743093.3770964

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3743093.3770964

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