DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion (2303.06840v2)

Abstract: Multi-modality image fusion aims to combine different modalities to produce fused images that retain the complementary features of each modality, such as functional highlights and texture details. To leverage strong generative priors and address challenges such as unstable training and lack of interpretability for GAN-based generative methods, we propose a novel fusion algorithm based on the denoising diffusion probabilistic model (DDPM). The fusion task is formulated as a conditional generation problem under the DDPM sampling framework, which is further divided into an unconditional generation subproblem and a maximum likelihood subproblem. The latter is modeled in a hierarchical Bayesian manner with latent variables and inferred by the expectation-maximization (EM) algorithm. By integrating the inference solution into the diffusion sampling iteration, our method can generate high-quality fused images with natural image generative priors and cross-modality information from source images. Note that all we required is an unconditional pre-trained generative model, and no fine-tuning is needed. Our extensive experiments indicate that our approach yields promising fusion results in infrared-visible image fusion and medical image fusion. The code is available at \url{https://github.com/Zhaozixiang1228/MMIF-DDFM}.

• The paper introduces DDFM, a novel framework that decomposes image fusion into unconditional generation and maximum likelihood subproblems using a pre-trained DDPM.
• Evaluation on infrared-visible and medical imaging datasets shows that DDFM outperforms state-of-the-art methods in metrics like entropy, mutual information, and SSIM.
• The research highlights DDFM’s potential for scalable, resource-efficient, and real-time applications in medical imaging and vision enhancement.

An In-depth Analysis of "DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion"

The paper "DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion" presents a comprehensive paper of multi-modality image fusion, specifically focusing on integrating information from different image modalities to improve the quality of the resultant fused images. By leveraging the Denoising Diffusion Probabilistic Model (DDPM), this research addresses the challenges associated with GAN-based methods, such as lack of interpretability and unstable training, offering a novel approach to enhance image fusion.

Methodology

The authors propose a conditional generation framework under the DDPM that divides the fusion task into two sub-tasks: an unconditional generation subproblem and a maximum likelihood subproblem. This hierarchical approach uses a pre-trained DDPM to model the natural image prior, enabling the synthesis of visually coherent images without the necessity of fine-tuning. The fusion process is accomplished through iterative sampling, where the DDPM provides generative priors, and the maximum likelihood subproblem is modeled using latent variables inferred through the EM algorithm.

Numerical Insights and Comparisons

The empirical evaluation of the proposed DDFM method is rigorous and extensive, involving multiple datasets for infrared-visible (IVF) and medical image fusion (MIF) tasks. In comparison to several state-of-the-art methods—including GAN-based and discriminative approaches—the proposed method demonstrates superior performance across most metrics, such as entropy (EN), standard deviation (SD), mutual information (MI), and structural similarity index measure (SSIM). Specifically, the authors report significant improvements in MI and Qabf, suggesting that the fused images not only retain essential information but also align well with human visual perception.

Implications and Future Directions

By successfully integrating generative priors with information from source images, DDFM sets a new benchmark for image fusion, revealing the potential to generate images across different modalities without sacrificing interpretability. The implications of this are particularly valuable in areas such as medical imaging and device-based vision enhancement, where detail preservation alongside feature integration is paramount.

The introduction of DDFM heralds a shift in multi-modal image fusion research, particularly when considering future developments. It suggests possible expansion into real-time applications with varying conditions, such as dynamic range modifications and real-time video fusion. The method's reliance solely on a pre-trained model without further fine-tuning presents a streamlined pipeline that promises to be resource-efficient and scalable.

Overall, the paper crafted by the authors stands as a seminal addition to the library of advanced AI methodologies in image processing tasks. It sets a foundation upon which further optimizations and extensions can be explored, including the integration of DDPM with other probabilistic models to further refine fusion results in complex, information-rich environments.