NeurIPS 2025

Projection-Manifold Regularized Latent Diffusion for
Robust General Image Fusion
Lei Cao1,
Hao Zhang1,2,*,
Chunyu Li1,
Jiayi Ma1,*
1Electronic Information School, Wuhan University, Wuhan, China
2Suzhou Institute, Wuhan University, Suzhou, China
whu.caolei@whu.edu.cn,
{zhpersonalbox, licy0089, jyma2010}@gmail.com
Abstract
This study proposes PDFuse, a robust, general training-free image fusion frame-
work built on pre-trained latent diffusion models with projection–manifold regu-
larization. By redefining fusion as a diffusion inference process constrained by
multiple source images, PDFuse can adapt to varied image modalities and produce
high-fidelity outputs utilizing the diffusion prior. To ensure both source consistency
and full utilization of generative priors, we develop novel projection–manifold
regularization, which consists of two core mechanisms. On the one hand, the Multi-
source Information Consistency Projection (MICP) establishes a projection system
between diffusion latent representations and source images, solved efficiently via
conjugate gradients to inject multi-source information into the inference. On the
other hand, the Latent Manifold-preservation Guidance (LMG) aligns the latent
distribution of diffusion variables with that of the sources, guiding generation to
respect the model’s manifold prior. By alternating these mechanisms, PDFuse
strikes an optimal balance between fidelity and generative quality, achieving su-
perior fusion performance across diverse tasks. Moreover, PDFuse constructs
a canonical interference operator set. It synergistically incorporates it into the
aforementioned dual mechanisms, effectively leveraging generative priors to ad-
dress various degradation issues during the fusion process without requiring clean
data for supervising training. Extensive experimental evidence substantiates that
PDFuse achieves highly competitive performance across diverse image fusion tasks.
The code is publicly available at https://github.com/...