GrFormer: A Novel Transformer on Grassmann Manifold for Infrared and Visible Image Fusion
Journal:
arXiv
Published Date:
Jun 17, 2025
Abstract
In the field of image fusion, promising progress has been made by modeling
data from different modalities as linear subspaces.
However, in practice, the source images are often located in a non-Euclidean
space, where the Euclidean methods usually cannot
encapsulate the intrinsic topological structure. Typically, the inner product
performed in the Euclidean space calculates the algebraic
similarity rather than the semantic similarity, which results in undesired
attention output and a decrease in fusion performance.
While the balance of low-level details and high-level semantics should be
considered in infrared and visible image fusion task. To
address this issue, in this paper, we propose a novel attention mechanism
based on Grassmann manifold for infrared and visible
image fusion (GrFormer). Specifically, our method constructs a low-rank
subspace mapping through projection constraints on the
Grassmann manifold, compressing attention features into subspaces of varying
rank levels. This forces the features to decouple into
high-frequency details (local low-rank) and low-frequency semantics (global
low-rank), thereby achieving multi-scale semantic
fusion. Additionally, to effectively integrate the significant information,
we develop a cross-modal fusion strategy (CMS) based on
a covariance mask to maximise the complementary properties between different
modalities and to suppress the features with high
correlation, which are deemed redundant. The experimental results demonstrate
that our network outperforms SOTA methods both
qualitatively and quantitatively on multiple image fusion benchmarks. The
codes are available at https://github.com/Shaoyun2023.
