Projection-Manifold Regularized Latent Diffusion for Robust General Image Fusion

This study proposes PDFuse, a robust, general training-free image fusion framework built on pre-trained latent diffusion models with projection-manifold regularization. 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 Multisource 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.