DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion
- URL: http://arxiv.org/abs/2303.06840v2
- Date: Tue, 22 Aug 2023 23:19:03 GMT
- Title: DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion
- Authors: Zixiang Zhao, Haowen Bai, Yuanzhi Zhu, Jiangshe Zhang, Shuang Xu,
Yulun Zhang, Kai Zhang, Deyu Meng, Radu Timofte, Luc Van Gool
- Abstract summary: We propose a novel fusion algorithm based on the denoising diffusion probabilistic model (DDPM)
Our approach yields promising fusion results in infrared-visible image fusion and medical image fusion.
- Score: 144.9653045465908
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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}.
Related papers
- Conditional Controllable Image Fusion [56.4120974322286]
conditional controllable fusion (CCF) framework for general image fusion tasks without specific training.
CCF employs specific fusion constraints for each individual in practice.
Experiments validate our effectiveness in general fusion tasks across diverse scenarios.
arXiv Detail & Related papers (2024-11-03T13:56:15Z) - MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling [64.09238330331195]
We propose a novel Multi-Modal Auto-Regressive (MMAR) probabilistic modeling framework.
Unlike discretization line of method, MMAR takes in continuous-valued image tokens to avoid information loss.
We show that MMAR demonstrates much more superior performance than other joint multi-modal models.
arXiv Detail & Related papers (2024-10-14T17:57:18Z) - CoMoFusion: Fast and High-quality Fusion of Infrared and Visible Image with Consistency Model [20.02742423120295]
Current generative models based fusion methods often suffer from unstable training and slow inference speed.
CoMoFusion can generate the high-quality images and achieve fast image inference speed.
In order to enhance the texture and salient information of fused images, a novel loss based on pixel value selection is also designed.
arXiv Detail & Related papers (2024-05-31T12:35:06Z) - DiffHarmony: Latent Diffusion Model Meets Image Harmonization [11.500358677234939]
Diffusion models have promoted the rapid development of image-to-image translation tasks.
Fine-tuning pre-trained latent diffusion models from scratch is computationally intensive.
In this paper, we adapt a pre-trained latent diffusion model to the image harmonization task to generate harmonious but potentially blurry initial images.
arXiv Detail & Related papers (2024-04-09T09:05:23Z) - Denoising Diffusion Bridge Models [54.87947768074036]
Diffusion models are powerful generative models that map noise to data using processes.
For many applications such as image editing, the model input comes from a distribution that is not random noise.
In our work, we propose Denoising Diffusion Bridge Models (DDBMs)
arXiv Detail & Related papers (2023-09-29T03:24:24Z) - Equivariant Multi-Modality Image Fusion [124.11300001864579]
We propose the Equivariant Multi-Modality imAge fusion paradigm for end-to-end self-supervised learning.
Our approach is rooted in the prior knowledge that natural imaging responses are equivariant to certain transformations.
Experiments confirm that EMMA yields high-quality fusion results for infrared-visible and medical images.
arXiv Detail & Related papers (2023-05-19T05:50:24Z) - DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion [7.06521373423708]
Denosing diffusion model, as a generative model, has received a lot of attention in the field of image generation.
We introduce diffusion model to the image fusion field, treating the image fusion task as image-to-image translation.
Our method can inspire other works and gain insight into this field to better apply the diffusion model to image fusion tasks.
arXiv Detail & Related papers (2023-04-10T12:28:27Z) - Image Generation with Multimodal Priors using Denoising Diffusion
Probabilistic Models [54.1843419649895]
A major challenge in using generative models to accomplish this task is the lack of paired data containing all modalities and corresponding outputs.
We propose a solution based on a denoising diffusion probabilistic synthesis models to generate images under multi-model priors.
arXiv Detail & Related papers (2022-06-10T12:23:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.