Work

MRMAFusion: A Multi-Scale Restormer and Multi-Dimensional Attention Network for Infrared and Visible Image Fusion

Year: 2026

Type: article

Abstract: Infrared and visible image fusion improves the visual representation of scenes. Current deep learning-based fusion methods typically rely on either convolution operations for local feature extraction or Transformers for global feature extraction, often neglecting the contribution of multi-scale features to fusion performance. To address this limitation, we propose MRMAFusion, a nested connection model that relies on the multi-scale restoration-Transformer (Restormer) and multi-dimensional attention. We construct an encoder–decoder architecture on UNet++ network with multi-scale local and global feature extraction using convolution blocks and Restormer. Restormer can provide global dependency and more comprehensive attention to texture details of the target region along the vertical dimension, compared to extracting features by convolution operations. Along the horizontal dimension, we enhance MRMAFusion’s multi-scale feature extraction and reconstruction capability by incorporating multi-dimensional attention into the encoder’s convolutional blocks. We perform extensive experiments on the public datasets TNO, NIR and RoadScene and compare with other state-of-the-art methods for both objective and subjective evaluation.

Source: Applied Sciences

DOI: 10.3390/app16020946

Authors: Liang Dong, Guiling Sun, Haicheng Zhang, Wenxuan Luo

Institution: Nankai University

Language: English

Topic: Advanced Image Fusion Techniques

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: Natural Science Foundation of Tianjin City