Efficient Rectified Flow for Image Fusion
Authors: Zirui Wang
Jiayi Zhang
Tianwei Guan
Yuhan Zhou
Xingyuan Li
Minjing Dong
Jinyuan Liu
Venue: NeurIPS 2025 poster ()
Keywords: Image Fusion
Multi-modality Image Fusion
Infrared and Visible Image Fusion
Primary area: applications

Abstract: Image fusion is a fundamental and important task in computer vision, aiming to combine complementary information from different modalities to fuse images. In recent years, diffusion models have made significant developments in the field of image fusion. However, diffusion models often require complex computations and redundant inference time, which reduces the applicability of these methods. To address this issue, we propose RFfusion, an efficient one-step diffusion model for image fusion based on Rectified Flow. We incorporate Rectified Flow into the image fusion task to straighten the sampling path in the diffusion model, achieving one-step sampling without the need for additional training, while still maintaining high-quality fusion results. Furthermore, we propose a task-specific variational autoencoder (VAE) architecture tailored for image fusion, where the fusion operation is embedded within the latent space to further reduce computational complexity. To address the inherent discrepancy between conventional reconstruction-oriented VAE objectives and the requirements of image fusion, we introduce a two-stage training strategy. This approach facilitates the effective learning and integration of complementary information from multi-modal source images, thereby enabling the model to retain fine-grained structural details while significantly enhancing inference efficiency. Extensive experiments demonstrate that our method outperforms other state-of-the-art methods in terms of both inference speed and fusion quality.

Efficient Rectified Flow for Image Fusion
Zirui Wang', Jiayi Zhang", Tianwei Guan', Yuhan Zhou?, Xingyuan Li", Minjing , Jinyuan Liu
City University of Hong Kong Dalian University of Technology Chinese University of Hong Kong ZZhjiiang University zrwang23-con cityu. edu. hk Segruan_1xy@1633
Abstract
Image fusion is a fundamental and important task in computer vision, aiming to combine complementary information from different modalities to fuse images. In recent years, diffusion models have made significant developments in the field of image fusion. However, diffusion models often require complex computations and redundant inference time, which reduces the applicability of these methods. To address this issue, we propose RFfusion, efficient one-step diffusion model for image fusion based on Rectified Flow. We incorporate Rectified Flow into the image fusion task to straighten the sampling path in the diffusion model, achieving one-step sampling without the need for additional training, while still maintaining high-quality fusion results. Furthermore, we propose task-specific Variational Au- toencoder (VAE) architecture tailored for image fusion, where the fusion operation is embedded within the latent space to further reduce computational complexity. To address the inherent discrepancy between conventional reconstruction-orieni VAE objectives and the requirements of image fusion, we introduce two-stage training strategy. This approach facilitates the effective learning and integration complementary information from multi-modal source images, thereby enabling the model to retain fine-grained structural details while significantly enhancing infer- ence efficiency. Extensive experiments demonstrate that our method outperforms other state-of-the-ar methods in terms of both inference speed and fusion quality. http:////..rr./////////eese
Code is available at
Introduction
In computer vision, image fusion is an important task aimed at merging two images from different modalities to obtain a fused image that contains complementary information from both modalities. Image fusion has wide applications across various scenarios. Infrared and visible image fusion (IVIF) 1 2 3 5 aims to enhance perception under adverse conditions by integrating the detailed information from visible images with the thermal radiation characteristics of infrared images. Medical image fusion (MIF) 7 focuses on mitigating the information discrepancies between MRI and CT modalities to provide more comprehensive and accurate diagnostic support. Multi-exposure image fusion (MEF) 8 9 and multi-focus image fusion (MFF) [10 I1] focus on merging images with different exposures and different focal planes, synthesize high-quality photographic images.
In recent years, with the advent Denoising Diffusion Probabilistic Models (DDPMs) [[2]. diffusion-basee methods have been widely adopted across various computer vision tasks, includ- ing image fusion. DDPMs learn the denoising process from noisy observations back to clean images the data distribution, thereby acquiring the ability to generate high-quality images.
Cooresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Compared to traditional fusion ap- -iffusion-based
Efficieny Comparison
proaches T
14].
Diff-IF Paramoher. 23.47M
not only ef- fectively integrate information from multiple source images but also signif- icantly enhance the visual quality of the fused results. Benefiting from the powerful priors encoded in pre-trained diffusion models, these approaches demonstrate great potential in multi- task fusion scenarios, where a single unified framework can be adapted to various fusion tasks with remarkable performance.
methods 15 16
18
Spatial Frequency (SF)
Ours
Purumeter. 65.57M
CCF Parmmstor 552. BIM
DDFM Parumeter 552.8I
DRMP
Paramoter: 170.9EM
Dif-Fusion
Parametor: 416.47M
Text-DiFuse Paramoter: 119.9M
10'
Average Runtime
Figure 1: Efficiency comparison with the state-of-the-at
diffusion-based methods.
Though diffusion-based methods have achieved remarkable progress in image fusion tasks, their long inference times pose significant challenges for real-world applications. Recently proposed approaches such as DDFM [5] and CCF [16] introduce fusion priors into the sampling process of diffusion models, effectively improving fusion quality. However, these methods typically require hundreds of sampling steps to achieve satisfactory results. Reducing the number of steps to improve efficiency often leads to a substantial drop in fusion performance. To accelerate inference in diffusion models, several strategies such as distillation and latent space diffusion have been widely explored. Nonetheless, their application to image fusion remains limited. First, existing distillation methods enable single-step sampling but usually require fine-tuning tailored to specific model architectures and datasets, lacking generalization across diverse fusion tasks. Second, while latent space diffusion methods based on Variational Autoencoder (VAE) can significantly reduce computational costs, their training objective primarily targets image reconstruction rather than image fusion, leading considerable challenges when applied directly to fusion scenarios. Therefore, it is imperative to develop sampling acceleration method that is tailored to image fusion tasks, capable of preserving fusion quality while maintaining generalizability.
To address these challenges, we propose novel method named RFfusion, which introduces the Rectified Flow mechanism into image fusion tasks for the first time. RFfusion significantly accelerates the inference process of diffusion models without requiring additional training and exhibits strong generalization across multiple fusion tasks. Specifically, we leverage Rectified Flow to construct a linear trajectory between the input images and the target fused image, embedding prior knowledge of the fused image during the sampling process to achieve efficient and high-quality single-step inference. Moreover, we incorporate the sampling process into the latent space and propose two- stage training strategy to address the objective mismatch between VAE reconstruction and fusion tasks. In the first stage. we fine-tune the VAE to better capture critical features needed for image fusion. In the second stage, the optimized VAE is integrated into the overall fusion framework for joint training, further enhancing the model's adaptability to fusion scenarios. Extensive experimental results demonstrate that RFfusion not only substantially reduces the number of inference steps and improves computational efficiency, but also outperforms existing state-of-the-ar methods across multiple standard image fusion benchmarks. Our contributions can be summarized as:
We propose a novel Efficient Rectified Flow image fusion (RFfusion) framework that enables one-step sampling across various fusion tasks without requiring additional training, significantly reducing computational cost and inference time while achieving high-quality fused images.
We introduce the image fusion task into the latent space to effectively reduce computational cost. To address the objective discrepancy between the reconstruction-oriien training of VAE and the specific requirements of image fusion, propose two-stage training strategy to enhance the VAE's adaptability to fusion tasks.
Extensive experiments demonstrate that our method significantly improves inference speed compared to other iiffuusionbbased approaches. Meanwhile, it also achieves superior fusion performance and shows strong adaptability across various fusion tasks, demonstrating excellent generalization capability..
2

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2 Related works
In this section, we first review influential image fusion algorithms from recent years. Then, we introduce the applications of Rectified Flow in various fields, especially in low-level vision.
Image fusion Image fusion combines images from different modalities to create single image with complementary information. Traditional methods [9] 20 21] use convolutional neural networks to achieve image fusion. The Transformer 22 23], has also advanced the field of image fusion, particularly when combined with CNNs for multi-modal fusion [24]. Recently, diffusion models have gained attention in low-level vision tasks [25 26 T5 T6 for their strong generative power, also being applied to image fusion tasks. DDFM [5]. using a Denoising Diffusion Probabilistic Model, has shown promising results in infrared-visible and medical image fusion but struggles with adapting to different scenarios. To address this, CCF [16] proposed a controllable diffusion-base fusion framework, which can optimize the fusion process but still faces challenges like excessive sampling steps.
Rectified flow Liu 27 first proposed the Rectified Flow method, which generates high-quality
images by straightening the path between two data distributions, requiring only one or a few sampling steps. InstaFlow (28] applies Rectified Flow to text-to-image (T2I) models, using the same approach
to straighten the trajectories of probability flows, enabling it to generate high-quality images in single step. FlowGrad [29] backpropagates gradients along the ODE trajectory, effectively enabling control over the generated content of a pre-trained Rectified Flow model. Recently, some Rectified
Flow-based methods (30 31
32 33 have also been applied to low-level vision tasks for model acceleration. FlowlE [31] constructs a linear many-to-one transport mapping using conditioned Rectified Flow to achieve efficient image enhancement. FluxSR [30] leverages Rectified Flow to distill diffusion model priors, enabling one-step real-world image super-resolution.
3 Preliminary
Rectified Flow Traditional diffusion models are trained by predicting the noise added during the forward process, enabling the model to generate high-quality images from Gaussian noise. However, this process typically requires multiple sampling steps, which significantly prolongs the inference time. In general, the forward process can be represented as:
2t a:To biE, E 1).
a₁ and by satisfy a: = 1, b, = 0 and 0g = 0, bt = In DDPM, this formula can be expressed as:
24 v 0, /1 a.c, c~ 10,
Different from DDPM, Rectified Flow treats the forward process as a transformation between two data distributions. which can be seen as transformation between Gaussian noise and the real image distribution in this case. The goal of Rectified Flow is to train model uy to predict the velocity we (xze along the path step as
(RF = Er,,, [[,, () uy (a:))1
Rectified Flow views the forward process as straight path between the real data distribution and the noise distribution, and its noise addition formula can be derived through linear interpolation. According to Equation [ we can derive:
Te. (1-t)xo te, e~ 1(0,
In this case, (xx)) can be expressed as:
c- = 3o- x ve (xxe))
(5)
1-
Therefore, according to Equation the training objective Rectified Flow can be derived as:
LR (0) Ey.z.. [1 () (e-xx))13 x0)1
(6)
By training neural network on large-scale dataset, the output of the network, up (xt, (), is encour- aged to closely match the training target zg- This enables the model find the shortest path between two data distributions, significantly accelerating the sampling process.
3

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(a) One Step Effective Rectified Flow Fusion Networks
Galdaacc
Overvien
fo tc,c~ N(0,
Fucion Priur
Imsage
Forward process
fi-A At v v(
VAR
Eneader
Sampling Rectified sampling
Visike
Sample with one step
image
(b) VAE Training stage
(c) VAE Training stage II
L mask
Image
BEE
Thme
Rec imoge
Ingut
Image Histogram
Image
Image Codebook
Parameters Frozen VAR Encoder
More
VAE
Parameters
Decader
Encoder
Decoder
Learnable
Figure 2: Illustration of the train and inference pipeline of our methods.
Variational Autoencoder In diffusion models, Variational Autoencoders (VAEs) are commonly employed to learn low-dimensional latent representations of data. By encoding images into latent spaces, VAEs enable the diffusion process to operate more compact latent space, significantly reducing computational costs while improving modeling efficiency. The training objective of a VAE can be formulated as:
arg min max Beup(() [Lvg(E, G, 2) + ACGAN ((E, G, D)) E,G.2
where E and G represent the encoder and decoder, respectively, Z denotes the discrete codebook, and D is the GAN discriminator. This formulation allows the VAE to produce compact yet semantically meaningful latent codes, which serve as an efficient and expressive latent space for subsequent diffusion modeling.
4 Methods
In this section, we first introduce how existing methods utilize diffusion models to achieve image fusion. Then, we describe how we incorporate Rectified Flow into the fusion task. Finally, we present the task-specific Variational Autoencoder (VAE) specifically designed for the fusion task, including the two-stage training strategy of VAE and the loss function used for guidance. The pipeline of our method is illustrated in Figure
4.1 Implementation of Fusion Methods in Diffusion Models
Previous image fusion methods based on diffusion models typically leverage prior knowledge acquired through pre-trained diffusion models to generate high-quality fused images. Inspired by the work presented in [34], these methods incorporate fusion image information into the sampling process via posterior sampling mechanisms of diffusion models, thus effectively guiding the generation of the fused image. During this sampling process, fusion information is progressively integrated and validated, ultimately achieving high-quality image fusion. The specific mathematical formulation can be expressed as follows:
po (.o)
=
(Sc- (e,i,0),
where fo the fused result and fr is the initial sampling image, usually Gaussian noise. Additionally, the corresponding posterior sampling can be solved using a Stochastic Differential Equation (SDE), and through Bayes' theorem, we can derive:
V log (r v) v (ot) v log [t),
where ,,, log Pf(fi) can be obtained via the SDE formulation, inspired by 34 V, log l() can be expressed as:
l fi) V, log For) p M(fo(f)))..
(10)

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Therefore, the fusion prior can be incorporated into the sampling process by computing the obser- vations || v - (f)(( between the fused image and the input images. In this manner, the high-quality image generation capability of diffusion models effectively leveraged to achieve image fusion. A more detailed derivation of the formulas is provided in Appendix A
4.2
One Step Effective Fusion Network
To enable efficient one-step image fusion, inspired by 35 we adopt Rectified Flow for the fusion task. Specifically, we utilize pre-trained model based on Rectified Flow sampling to generate high-quality fused images. Notably, we observe that using visible-light images as input, rather than pure Gaussian noise, leads to improved fusion performance. The sampling process in our method can be formally expressed as:
c Jt- = S Al v(se.) vx(st) - T- [1,
(11)
Followed by DDFM [1]. we incorporate the inference results of the Expectation-Maxinn algorithm into the sampling process of the diffusion model, thereby injecting the prior of the fused image into the diffusion model through posterior sampling, and achieving image fusion. This process can be formulated as:
(EM)
po i, ( p( f (Se At (o (,,))) dfc.
(12)
where p(fe) denotes the initial distribution, while the Dirac delta function ö ensures that the output fo is strictly determined by the input St and the velocity field vo(Se i, v). It important to note that Rectified Flow leverages Ordinary Differential Equation (ODE) framework, meaning that no stochastic noise is injected during the sampling process. Instead, data is deterministically transformed from an initial distribution to the target distribution by optimizing a continuous velocity field vo v). Consequently, Equation 9 in our method can be reformulated as
8y (feld. j v) = (f())) + Vr, log p(i, v fi) = (f()) + v. log v fop))
(13)
In this way, we transfer Rectified Flow to the image fusion task, achieving an efficient single-step image fusion method without requiring additional training.
4.3
VAE Autoencoder for Image Fusion
LDM 26 was the first to introduce generative diffusion models into the latent space, leveraging the powerful encoding capability of Variational Autoencoder (VAE) to perform image tasks in the latent space, significantly reducing inference costs while achieving high visual fidelity.
Inspired by this work, we introduce VAE into the image fusion task to enable image generation in the latent space. However, applying VAE-based approaches to image fusion faces two key challenges: (1) Previous methods for image reconstruction typically focus on pixel-level visual fidelity, whereas the core of image fusion lies in capturing complementary semantic information across different modalities. (2) Unlike traditional reconstruction tasks where the objective is to recover the original input, image fusion requires decoding fused image that integrates information from multiple input modalities. Due inherent differences between the input images and the desired fused output, this discrepancy poses a significant challenge for the direct application of pretrained VAE in image fusion tasks. To address the aforementioned challenges, we propose two-stage training strategy to effectively adapt VAE architectures for the image fusion task.
VAE training stage T To address Challenge I, devise training strategy based on frequency similarity. Specifically, prior research has demonstrated that the complementary semantic information emphasized in image fusion is often closely correlated with the high- and low-frequency components of the input images. Leveraging this insight, we introduce a frequency similarity loss and fine- tune only the VAE encoder and decoder, without involving Rectified Flow sampling or the image fusion process. As a result, the training procedure closely resembles that of conventional image reconstruction. Followed by Equation 7 the corresponding training goal is formulated follows:
(),))) D) + Aie Line (I, +)}, R = arg min max Exep((e [Evo E, G, 2) + AGAN (f E, G, E.G.Z,z D
(14)
5

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DRPM Xt
DDFM
DDFM
Imput
Imput
Input
CCT
OCE
CCF
Fffasion
CNNss
RFfusion
REficion
CNRS
RFfasion
Ous
Figure 3: Visual comparison of IVIF with SOTA methods on FD datasets.
where Lvg. LGAN are followed by [36]. The proposed frequency loss, Live is designed to capture discrepancies in the frequency domain by first transforming the input images from the spatial domain using the Fast Fourier Transform (FFT). The transformation process can be expressed as:
H-1W-1 ΣΣ (xr
in FF(n), F(ros),, F(u,
2eus
(15)
Next, we shift the zero-frequeney components of both in and lnc which represent the average intensity of the images the center of the spectrum, resulting in Im and L for loss computation. The loss can then be formulated as:
Lire NN (1og(1+)mntit))) (d) (log(1 1/ 1))b")))
(16)
where N.) denotes normalization operation. Optimizing the above losses encourages the VAE to focus on semantic information relevant to fusion during image reconstruction.
VAE training stage II To address Challenge II, we propose a training strategy for Variational Autoencoder (VAE) tailored to the image fusion task. Specifically, we integrate the VAE into the overall fusion framework and perform joint training to enhance its adaptability to the fusion process. is important to note that, in our method, the input is visible image, and prior information from the fused image is incorporated during the sampling stage to achieve the fusion. As result, the VAE encoder is only required to effectively compress the input image, while the decoder responsible for both reconstructing the image and incorporating fusion-related information. Therefore, in the second stage of training, we focus on fine-tuning the VAE decoder to improve its ability to reconstruct fused images. During this stage, we employ fusion-specific loss function commonly used in image fusion tasks to optimize the VAE, which is formulated as follows:
Liusion = Cint + ASSIM LSSIM + Agrai Coraad Acolor + Ammask Lrunsk
(17)
where Lim, LSSIM, Lyrad: and Leolor are followed by [37, Meanwhile, achieve ceffency-guidee regional fusion, we introduce saliency mask loss, denoted as Lmssk, which can be formulated as:
Lmask I,W, I + Wrr 1,r Iyll
(18)
Among them, Tir, To represent the input images, and 1 are the fused image. W., and Wir denote the saliency-based weight maps computed from the corresponding input images. The saliency mask loss Lmask guides the network to focus on salient regions during the fusion process, thereby improving the preservation of complementary information within the fused image. By jointly optimizing this loss with other fusion objectives, the reconstruction capability of the VAE decoder is significantly enhanced, ultimately leading high-quality image fusion results. More details about the training loss Liusion and Lmask can be found in Appendix B
5 Experiments
Experiment datasets We conduct experiments on three representative image fusion tasks: infrared and visible image fusion (IVIF), multi multi-exposure image fusion (MEF), and multi-focus image fusion (MFF). For the infrared and visible image fusion task, evaluations are performed on three widely- used benchmark datasets: FDD (1) TNO (38) and RoadScene [39 For the multi-exposure and
multi-focus fusion tasks, we utilize the MEFB 40 and MFIF 10 datasets, respectively. The MFIF
dataset includes the Lytro [1). MFFW 42 and MFI-WHU
43 datasets.

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Table 1: Comparison of Metrics with Our Baseline Method DDFM
15
MD Dataset
T&R Datasets
Methods
EN
M1
VIF
SSIM
EN
MI
SF
VIF
SSIM
DDFM
6.720
2.871
9.102
0.677
0.867
7.077
1.798
8.910
0.277
0.207
Ours
6.722
3.320
9.780
0.748
0.914
7.139
2.948
12.55
0.675
0,921
(+0.002)
(+0,449) (+0.678)
(+0.071)
(+0.047)
(+0.062)
(+1.150)
(+3.640)
(+0.398)
(+0.714)
Implementation Details The two-stage training of the VAE was conducted entirely on an NVIDIA V100 GPU, In the first stage, the model was trained the LLVIP 44 and MSRS 45 datasets for 20 epochs. Interestingly, the best validation performance was typically achieved within just 4 to epochs. The second stage involved training exclusively on the MSRS 451 dataset for 40 epochs. The remaining hyperparameters for both stages were configured in accordance with the experimental settings detailed in [26] and [37]. We evaluate our method on all three fusion tasks using the same set of checkpoints, without any task-specifi fine-tuning, thereby demonstrating the strong generalization capability of our approach across diverse tasks.
Table 2:
Quantitative comparison on FD, TNO, and RoadScene datasets. The best and second best results are highlighted in bold and underline.
Dataset
FD Dataset
T&R Dataset
Method
MI f
VIF
SCD
EN
MI
VIF 1
SCD
EN
U2Fusion
3
2.760
0.633
1.569
6.659
2.599
0.556
1.338
6.821
YDTR 46
3.183
0.635
1,506
6.547
2.976
0.588
1.420
6.842
UMFusion 47
3.089
0.613
1.570
6.669
2.888
0.610
1.475
6.967
ReCoNet
48
3.066
0.577
1.483
6.679
2.985
0.540
1,510
7.051
LRRNet
499
2.805
0.566
1,463
6.437
2.766
0.508
1.558
7.118
CoCoNet 50
2.631
0.729
1.772
7.738
2.579
0.568
1.782
7.735
DDFM 15
2.871
0.677
1,683
6.720
1.798
0.277
1.160
7.077
Ours
3.320
0.748
1.574
6.722
2.948
0.675
1.639
7.139
Table 3: Efficiency comparisons with other diffusion-based methods. The best and second best results are highlighted in bold and underline.
Methods
Metrics
DRMF
Dif-Fusion
Diff-IF
Text-DiFuse
DDFM
CCF
Ours
SF AGI
12.57
10.42
13.90
9.319
9.689
10.14
14.00
4.201
4.307
5.179
3.559
3.981
3.882
5.218
Runtime (s)
3.221
1.997
2.457
9.199
22.03
62.47
0.308
Parameters (M)
170.98
416.47
23.47
119.49
552.81
552.81
65.57
5.1
Experiments on Infrared and Visible Image Fusion
In this section, conduct comprehensive comparison between proposed RFfusion method and other fusion approaches. We begin by comparing with our baseline method, DDFM (15] Subsequently, we evaluate its performance against several state-of-the-art methods proposed in recent years to demonstrate the superiority of our approach, including: U2Fusion (3). YDTR [46).
UMFusion (47,, ReCoNet 448), LRRNet (49). CoCoNet (50), and DDFM (15.
Comparison with DDFM method Since our method built upon the DDFM framework by introduc- ing fusion priors to achieve image fusion with the main differences lying in the sampling strategy and the use of VAE for latent space generation we primarily compare our approach with DDFM. As shown in Table our method significantly accelerates inference and reduces computational overhead, while outperforming DDFM across all fusion metrics on multiple datasets. These results indicate that our method not only effectively reduces the number of sampling steps but also substantially enhances the quality of the fused images. Furthermore, the results validate the generality and flexibility of our approach, demonstrating its potential to serve Baug-and-plas module that can be integrated into
7

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DeFusion
Toput
Input
Source
Souree
CCF
Uluc
CCP
Deflusion
Tex: E
RFfusion
Dur
RElusiod
Soure
Source
CCF
Durs
Figure 4: Visual comparison of multi-exposure image fusion and multi-focus image fusion with SOTA methods on MEFB and MFIF datasets.
other diffusion-based image fusion frameworks to simultaneously improve both inference efficiency and fusion performance.
Quantitative Comparison As shown in Table 2 we conducted a comprehensive evaluation of the proposed method using four widely adopted quantitative metrics across three benchmark datasets: MDFD, TNO, and Roadscene. The results demonstrate that our method consistently ranks among the top across most metrics, indicating strong overall performance. Specifically, the MDDD dataset, our method achieves the best performance in terms Mutual Information (MI), highlighting its effectiveness in preserving informative content from the source images. Moreover, our method achieves the highest scores in Visual Information Fidelity (VIF) across all datasets, further confirming its superiority in enhancing the visual quality of the fused images.
Qualitative Comparison As shown in Figure 3 our method demonstrates superior visual perfor- mance compared to other approaches. We selected four images from the FD dataset for qualitative analysis, covering different scenarios including both daytime and nighttime, to ensure a compre- hensive evaluation. Our method better preserves detailed texture information from the original images, such as window textures on buildings and fine details at the ends of tree branches, whereas other methods tend blur these features. Additionally, effectively and better retains the mutual information between visible and infrared images. This demonstrates the advantages of method in qualitative results.
method highlights human details more
5.2 Experiments on efficiency comparisons with other diffusion-basee methods
To verify the effectiveness of our method in reducing the inference time and computational cost of diffusion models in image fusion tasks, compare it with several diffusion-basee image fusion approaches proposed recent years, including DRMF [5T], Dif-Fusion [8], Diff-IF (52), Text- DiFuse [17.. DDFM [15], and CCF [16]. All experiments conducted
NVIDIA V100 GPU, and the fusion speed as well as the number of model parameters are evaluated on the RoadScene [39 dataset to comprehensively assess the efficiency and complexity of each method. As shown in Table and Figure compared with other diffusion- significant advantage in inference speed while also achieving superior fusion quality. These results indicate that our method not only greatly improves inference efficiency but also maintains, or even enhances fusion performance, fully validating its capability for joint optimization of efficiency and effectiveness.
image fusion methods, our approach demonstrates
5.3 Experiments Evaluation Multi-Focus Fusion
Quantitative Comparison As shown in Table 6 conducted comprehensive evaluation of the proposed method on the MFIF dataset. The experimental results demonstrate that our method consistently ranks among the top two across most evaluation metrics, fully validating its superior effectiveness in the multi-focus fusion (MFF) task. Notably, the proposed method achieves this performance without any fine-tuning the multi-focu fusion dataset, further confirming its strong generalization ability and robustness across different tasks.
Qualitative Comparison As shown in Figure 6. we selected two representative images from the MFIF dataset for qualitative analysis, covering both daytime and nighttime scenarios to ensure comprehensive evaluation. Compared other methods, our method more effectively preserves

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Table 4: Quantitative comparison MEFB and MFIF datasets. The best and second best results highlighted in bold and underline.
Dataset
MEFB
Dataset Qeb f
MFIF
Dataset
Method
MI
C
PSNR 1
MI 1
CC
Qcb 1
PSNR 1
DeFusion
53
4.854
0.834
0.365
57.70
6.007
0.976
0.627
76.62
TC-MoA
54
5.418
0.900
0.430
59.00
6.686
0,968
0.731
74.78
Text-IF 337
5.596
0.860
0.385
56.44
5,399
0,967
0.629
71.74
DDFM [51
3,850
0.792
0.321
58.39
3.232
0.772
0.413
66.24
CCF 16
4.830
0.898
0.398
58.38
4.799
0.956
0.474
66.64
Ours
6.528
0.901
0.461
58.49
6.443
0.977
0.654
75.04
clear details from the original images, achieving high-quality multi-focus image fusion and fully demonstrating its superior performance in qualitative evaluation.
5.4 Experiments on Evaluation on Multi-Exposure Fusion
Quantitative Comparison Table presents a quantitative comparison between our method and existing approaches on the MEFB dataset. Notably, our method does not require any fine-tuning on the MEF dataset. consistently outperforms other multi-exposure fusion (MEF) methods across most evaluation metrics, demonstrating its superior performance and strong generalization capability the multi-exposure image fusion task.
Qualitative Comparison As shown in Figure compared to other methods, our fusion results better preserve the detailed features of the original images and achieve superior visual performance. We selected two images from the MEFB dataset for qualitative analysis. Our method more effectively retains the texture of windows and the contour features of candles, demonstrating its advantages in qualitative evaluation.
Table 5: Ablation studies on the effectiveness of the two-stage training strategy and loss functions.
Stage
Stage II
PSNR
MI
SF
AG
Lys
Lmak
PSNR
MI
SF
AG
59.41
2.998
12.16
4.615
57.22
2.944
12.77
4.882
59.68
3,017
12.88
4.676
58.84
3.202
13.56
5,021
60.36
3.001
12.57
4.783
59.67
3.121
13.31
4.976
61.81
3.220
14.00
5.218
/
61.81
3.220
14.00
5.218
5.5 Ablation Study
Experimental on the effectiveness of the two-stage training strategy. We conducted ablation studies the two-stage training strategy for the VAE to evaluate its effectiveness in the image fusion task. As shown T we compared fusion performance under four settings: without any training, using only the first-stage training, using only the second-stage training, and applying both stages of training. The results demonstrate that the fusion performance is optimal when both stages are applied, validating the effectiveness of the training strategy in enhancing fusion quality.
Experimental on the effectivvness of the loss functions. We conducted ablation experiments on the loss functions used in our proposed training method to evaluate their contributions to image fusion performance, as shown in Table [ Specifically, we designed four experimental settings: without using either Live or Lmusk, using only Lire, using only Lmask, and using both Live and Lmask ehperimeennt improve fusion quality, while their combined use leads to the best performance. These findings validate the effectiveness and necessity of the proposed loss function design.
simultaneously. The experimental results demonstrate that both Liee and Lmask can independently
Limitation
RFfusion still relies Rectified Flow pre-trained model trained on generic image generation tasks, which is not specifically designed for image fusion. This limitation may hinder the further improvement of RFfusion's 's fusion performance.

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7 Conclusion
In this paper, we propose an efficient one-step diffusion-based image fusion method called RFfusion. By integrating Rectified Flow into the image fusion task, our method leverages its efficient one-step sampling mechanism to significantly accelerate the diffusion-basse fusion process. Moreover, we design task-specific Variational Autoencoder (VAE) that performs fusion in the latent space, effec- tively reducing computational overhead while preserving more image details. Extensive experimental results demonstrate that RFfusion achieves superior performance in both inference speed and fusion quality compared to existing gate-offttheaat methods, and also exhibits strong generalization capabil- ities across diverse image fusion tasks. In the future, we will further explore acceleration mechanisms of diffusion models image fusion tasks to achieve more efficient image fusion methods
Acknowledgments: This work was partially supported by the National Natural Science Foun- dation of China 65.62202078 and No.62372080), and China Postdoctoral Science Foundation
(2023M730741).
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20

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Appendix
A Detailed derivation of formulas
In this section, we provide a detailed proof regarding posterior sampling in Liffusion-basee image fusion methods. The noise predicted by the diffusion model at time step t is often related to the score at the current time step. According to [55]. the specific formulation can be expressed as:
ET( -VI- at Vx, log p(xz),
(19)
In posterior sampling, we also need to take into account the guidance from image fusion, denoted as i, v. Therefore, what we need to solve is v, log p(al(, v), which can be expressed using Bayes' theorem as:
Pe(fe (1,) Pr(i, ft) Po(fe)
(r((,
lo (Je. (,,) = log v ft) + logp - (e,, 5 log (ffe i, V, log (ft) + v log ft).
(20)
Among them, F log p(fe v) and y, log p(f:) can be expressed by Equation 19 as follows:
(o(fe,t) t) = - - a4 V pt
(feet i, - - 04 V 4 De (fe 8,
(21)
Therefore, the final equation can be expressed as:
(o(t,t) v a V f pe | St): x (f((tttt - v1 a ,, lo foj);
(e(((,)) -2 ar V
(22)
Therefore, we inject the image fusion prior by correcting the predicted noise during the sampling process, thereby achieving high-quality image fusion based the diffusion model.
B Details about the training loss
In the main text, the loss Liusion used in the training stage II is defined as follows:
Lrusion Xinthin ASSIM ESSIM + Agraa Azolor + Amask maskk-
(23)
Followed by (37]. we use the Intensity Loss to encourage the model to focus on salient features the fused image, which specifically defined as:
Lini
I - max (T,, 11)
(24)
HW
Here, 1 and 1 are the ground truth corresponding to the fused image. We also use LSSIM to train vis the model so that the fused image is structurally similar as possible to the two input images. It is defined as:
CSSI (1 UL-SS1M(4, 1 a(1 (1,,
(25)
We also compute the gradient loss firaa to ensure the similarity between the fused image and the input images in terms of edge features:
Carad
HW IVI, max (VI. vIgI
(26)
and use Cootor to keep consistent color with input images:
Looter
HW |FFosc((T) Fosor (19 Il
(27)
As mentioned in the main text, we use Lmask for saliency-guided regional fusion, which can be represented as:
Lmask ,, I, + Wor 1,r 1yll2.
(28)
21

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Here, W. and Wi denote the saliency-bare based weight maps computed from the corresponding input images. W, and Wi, are computed based on pixel-level visual saliency maps. Specifically, they are estimated by measuring the sparsity of the image pixel histograms: the sparser the pixel distribution, the higher the corresponding saliency. The computation can be formulated as:
255
Saliency(1)
Σ - Hist(j). j=0
(29)
Here, i represents the grayscale value of the current pixel, and j denotes the grayscale value of the traversed pixels. By computing the saliency values (i.ji and (i.).) for each pixel, we can obtain the corresponding W. and Wi. The specific formulas are given as:
(.,i.) = pv + S,(i.) Ho Sir (1,
(30)
(i,( = -W,(1,)
(31)
C Additional experiments of our method
Table 6: Quantitative comparison Lytro, MFFW and MFI-WHU datasets. The best and second best results are highlighted in bold and underline.
Dataset
Lytro
MFFW
MFI-WHU
Method
MI
CC
Qch
PSNR
MI
CC
Qcb
PSNR
MI
CC
Qch
PSNR
DeFusion
53
6.27
0.97
0.59
77.2
5.59
0.97
0.55
74.4
6.01
0.97
0.69
77.2
TC-MoA
7.45
0.97
0.76
74.8
5.34
0.96
0.63
72.8
6.76
0.97
0.75
75.6
Text-IF 37
5.63
0.97
0.65
71.9
5.26
0.96
0.61
70.2
5.31
0.97
0.62
72.3
DDFM [5]
3.53
0.85
0.41
67.2
3.33
0.73
0.38
64.5
2.99
0.74
0.43
66.4
CCF
16
5.15
0.96
0.49
66.8
4.47
0.95
0.47
66.6
4.95
0.96
0.47
67.1
Ours
6.58
0.98
0.61
76.1
5.80
0.97 0.55
71.7
6.38
0.98
0.71
75.7
More Quantitative Comparison of Multi-Focus Fusion As shown in Table 6 we conducted
comparisons three datasets in MFIF: Lytro [4], MFFW [42,, and MFI-WHH 4 Our method outperforms other approaches on most metrics. Specifically, achieves either the first or second best performance 9 different metries, surpassing other comparison methods and demonstrating the superiority our approach in the Multi-Focus Fusion task.
"Compare the percentiles of the same value.
22


Reviews:
[{"review_id": "f5NFhHvYd1", "reviewer": "Reviewer_uZpR", "summary": "This paper proposes a method that leverages Rectified Flow for latent space sampling to accelerate image fusion inference. It introduces VAE to bring the image fusion process into the latent space and employs a two-stage finetuning strategy to adapt the VAE prior to the fusion task, further enhancing inference speed.", "questions": "1) The paper mentions that using visible images as the starting point for sampling better results than using Gaussian noise. Please explain the reason behind this choice.\n2) In the first stage of VAE training, the authors used the LLVIP and MSRS datasets, while in the second stage only the MSRS dataset was used. Please explain the rationale behind this setup.\n3) Please elaborate on the details of the VAE training process, such as the computational cost of the two-stage VAE training, and which type of pre-trained VAE prior is used for finetuning.", "limitations": "see weakness", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1) RFfusion successfully addresses the inference time issues of previous diffusion-based methods by significantly accelerating the inference process while maintaining impressive fusion performance.\n2) The paper is generally well-structured and coherent, with smooth writing and clear, visually appealing figures.\n\nWeaknesses:\n1) Although RFfusion effectively improves inference speed, the quantitative fusion performance presented in the paper does not appear to show a definitive advantage over other methods.\n2) RFfusion is only built upon DDFM and has not been validated on other diffusion-based methods, making it difficult to substantiate the generalization capability claimed in the paper.\n3) Although RFfusion employs both VAE and Rectified Flow to accelerate inference, the paper lacks ablation studies to separately validate the individual contributions of VAE and Rectified Flow to the overall speed improvement.", "was_revised": true, "final_justification": "The author's answer solved my concerns, so I decided to give it a higher score.", "rebuttal": "Thank you for the valuable suggestions. We appreciate the reviewer’s recognition of **the performance improvements** achieved by our method. In response to the questions raised by the reviewer, we have provided the following answers, and we hope they will address the concerns.\n\n- **W1: Quantitative fusion performance lacks a clear advantage over other methods.**\n\nThanks for your comment. We acknowledge that, compared to other fusion methods, our approach does not demonstrate an absolute and comprehensive advantage across all evaluation metrics. However, in terms of the overall average performance, our method still maintains a leading position. More importantly, rather than merely surpassing existing methods in fusion quality, we aim to emphasize **the significant improvement in fusion speed** achieved by our approach. Compared to the baseline, our method brings substantial enhancements in both **inference speed** and **various performance metrics**. Therefore, we believe that even if more effective diffusion model fusion methods are proposed in the future, our approach can be readily adapted to them and will likely achieve even better results in both speed and effectiveness. This is the core contribution of our work.\n\n- **W2: Lacking validation on other diffusion-based methods.**\n\nWe have supplemented our method by integrating it with CCF [1] as the baseline to verify the generalization capability of our approach. The experimental results are shown in the table.\n\n| RoadScene | TE | Qcb | EN | MI | SF | AG | PSNR |\n|-------------|--------|--------|--------|--------|--------|--------|--------|\n| CCF-200 | 6.98  | 0.43 | **7.36** | **2.88**  | 10.1  | 3.88 | **63.3**\n| CCF-1 | **7.33** | **0.45** | 7.01 | 2.31 | **11.3**  | **5.23**  | 58.4\n\nAs shown in the table, our method accelerates CCF from **200 steps** sampling to **single step** sampling. Although the performance is somewhat reduced compared to the original CCF method, it still demonstrates the great potential of our method for integration with other diffusion model fusion methods.\n\n- **W3: Lacking ablation studies to isolate the individual contributions of VAE and Rectified Flow to inference speed improvement.**\n\nWe have added ablation experiments on the inference speed improvements of VAE and Rectified Flow. The experimental results are as follows, and we will update the article with these results in a later version.\n\n| RF | VAE | Runtime(s)\n|-------------|--------|--------|\n| - | - | 22.03\n| - | ✓ | 0.650\n| ✓ | ✓| **0.308**\n\nThe table clearly shows that both VAE and Rectified Flow contribute to **improved inference speed**, which validates the argument presented in our method.\n\n- **Q1: Why use visible images as the sampling starting point?**\n\nCompared to the traditional DDPM algorithm, Rectified Flow focuses more on **direct flow matching between image distributions** and therefore no longer relies on Gaussian noise as a necessary component. By starting the sampling process from a visible image, the model can leverage the visible prior, enabling the generation of more informative and richer fused images.\n\n- **Q2: Only using the MSRS dataset in the second stage.**\n\nIn the second stage of VAE training, we introduce the RFfusion sampling process. Since this process relies on inference operations, the training must be conducted with a relatively **small batch size**, and the overall **training time increases significantly**. The primary objective of stage two is to optimize the VAE decoder's ability to **reconstruct fused images**, rather than to focus on the generalization performance of the fusion method across multiple datasets. Therefore, we choose to train on a relatively small dataset, which significantly reduces the training time without compromising the final performance of our method.\n\n-**Q3: The paper should clarify the VAE training details.**\n\nIn the first stage of training, we trained for a total of **20 epochs** on the LLVIP [2] and MSRS [3] datasets. However, we observed that the model typically converged within **4 to 5 epochs**, which took approximately **4 to 5 hours** on a V100 GPU. In the second stage, we trained for **40 epochs** on the MSRS dataset, with the entire process taking about **one day**. The pre-trained VAE we used was the VQ-VAE from LDM [4], with a **downsampling factor of f = 4** and equipped with an **attention mechanism**.\n\nReference\n\n[1] Cao B, Xu X, Zhu P, et al. Conditional controllable image fusion[J]. Advances in Neural Information Processing Systems, 2024, 37: 120311-120335.\n\n[2] Jia X, Zhu C, Li M, et al. LLVIP: A visible-infrared paired dataset for low-light vision[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2021: 3496-3504.\n\n[3] Tang L, Yuan J, Ma J. Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network[J]. Information Fusion, 2022, 82: 28\n\n[4] Rombach R, Blattmann A, Lorenz D, et al. High-resolution image synthesis with latent diffusion models[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022: 10684-10"}, {"review_id": "pYq2JtiAew", "reviewer": "Reviewer_fHBh", "summary": "This paper proposes RFfusion, an efficient image fusion method based on rectified flow. RFfusion leverages rectified flow to achieve one-step sampling for image fusion. In addition, it incorporates a variational autoencoder (VAE) to accelerate the fusion process and introduces a two-stage training strategy specifically designed to adapt to the image fusion task. These components work together to significantly enhance both the speed and quality of the fusion results.", "questions": "1) The authors mentioned using a pretrained model from rectified flow in their paper. Could the authors provide detailed information about the specifics of this pretrained model?\n2) Could the authors provide the performance of the proposed method on additional datasets and other fusion tasks to better demonstrate its generalization capability?\n3) The authors employed additional loss functions during VAE training, as shown in Eq. (17). Could the authors explain the rationale behind choosing these specific losses to finetune the VAE?", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\n1) Compared to previous methods, the author's proposed approach significantly accelerates image fusion speed and achieves leading performance across multiple tasks.\n2) The authors introduced rectified flow and VAE into the image fusion task, achieving promising results and providing inspiration for subsequent research.\n3) The authors highlight that their method is adaptable to various fusion tasks and can be transferred to other diffusion-based image fusion approaches, demonstrating exciting generalization potential.\n\nWeaknesses:\n1) The authors validate their method exclusively using the baseline DDFM approach. Given that other approaches, such as CCF, also utilize similar image fusion strategies, it would strengthen the paper if the authors could demonstrate the effectiveness of incorporating rectified flow and VAE into these alternative methods to better establish the generalizability of their proposed technique.\n2) The authors did not provide performance evaluations of their method on additional image fusion tasks or datasets, such as medical image fusion, thus failing to comprehensively demonstrate the superiority of their approach in broader scenarios.\n3) The authors primarily explain why their method can accelerate the original approach; however, according to the results presented in the paper, the proposed method also surpasses DDFM in quantitative metrics. The authors have not provided a detailed explanation for the observed performance improvements.", "was_revised": true, "final_justification": "Previous concerns have been addressed. I decided to keep my original score as Accept.", "rebuttal": "Thank you for the valuable comments. We appreciate the reviewer’s recognition of **the excellent fusion performance** and **generalization ability** of our method. Below are our responses to the reviewer’s questions.\n\n- **W1: Validating the proposed technique on additional fusion methods.**\n\nThanks for your comments. About weakness 1, you can refer to weakness 2 of **Reviewer uZpR**.\n\n- **W2: The lack of evaluations on additional tasks or datasets.**\n\nWe have supplemented our method's performance on medical image fusion. Specifically, we conducted tests on the **Harvard Medical Image Dataset**, which includes **MRI-CT, MRI-PET, and MRI-SPECT** image pairs. The fusion results are shown in the table.\n\n| MRI-CT | MI | AG | SF | EN | SSIM | PSNR | \n|-------------|--------|--------|--------|--------|--------|--------|\n| DDFM [1] | 3.29  | 4.62 | 19.1 | 4.18  | 1.19  | 63.3 | \n| Ours | **3.95**  | **5.49** | **23.6** | **4.62**  | **1.21**  | **64.4**  | \n\n| MRI-PET | MI | AG | SF | EN | SSIM | PSNR | \n|-------------|--------|--------|--------|--------|--------|--------|\n| DDFM [1] | 2.82  | 4.60  | 17.4  | 3.80  | **1.37**  | **65.5** | \n| Ours | **3.11**  | **5.02**  | **19.6** | **3.83**  | 1.01  | 64.8 | \n\n| MRI-SPECT | MI | AG | SF | EN | SSIM | PSNR | \n|-------------|--------|--------|--------|--------|--------|--------|\n| DDFM [1] | 3.04  | **3.77**  | **14.7**  | 3.63  | **1.43**  | 67.4 | \n| Ours | **3.24**  | 3.37 | 14.3 | **3.68**  | 1.33  | **68.1**  | \n\nAs shown in the tables, our method consistently outperforms the baseline DDFM on the MRI-CT fusion task, demonstrates a clear advantage on MRI-PET, and achieves comparable performance to DDFM on MRI-SPECT. Overall, our approach exhibits superior performance in medical image fusion compared to DDFM. Notably, our method requires **no additional training** and offers **faster inference**, further highlighting its great potential for medical image fusion applications.\n\n- **W3: Why RFfusion also achieves better quantitative performance than DDFM?**\n\nOur method RFfusion, employs a VAE for **image reconstruction** during the fusion process and is trained on a fusion dataset to better adapt to the fusion task, enabling **high-quality image fusion**. As a result, we achieve improvements in evaluation metrics compared to DDFM. In addition, the pre-trained generative model used in RFfusion provides **better image generation performance** than that of DDFM, which further contributes to the superior performance of our method in terms of quantitative metrics.\n\n- **Q1: Detailed information about the pretrained Rectified Flow.**\n\nWe use **checkpoint 12** from the Rectified Flow [2] as the pretrained model, which is trained on the **LSUN Church dataset** with a resolution of **256×256**.\n\n- **Q2: Evaluating the proposed method on additional datasets and fusion tasks.**\n\nPlease refer to weakness 2.\n\n- **Q3: The rationale behind the choice of additional loss functions.**\n\nThese loss functions are incorporated into the VAE training process to enhance the reconstruction quality of the fused images. They evaluate whether the fused image effectively preserves the critical information from the input images from multiple perspectives, including **structure, color, and saliency**. By introducing diverse loss terms during training, the model not only focuses on the overall image quality but also better retains **the essential information from multiple sources**, thereby improving the comprehensive performance of the fusion results.\n\n**Reference**\n\n[1] Zhao Z, Bai H, Zhu Y, et al. DDFM: denoising diffusion model for multi-modality image fusion[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2023: 8082-8093.\n\n[2] Liu X, Gong C, Liu Q. Flow straight and fast: Learning to generate and transfer data with rectified flow[J]. arXiv preprint arXiv:2209.03003, 2022."}, {"review_id": "RDw4Q1bDPd", "reviewer": "Reviewer_oayV", "summary": "This article proposes an efficient one-step image fusion method RFfusion, which combines Rectified Flow and task specific VAE structure, aiming to improve the inference efficiency of image fusion while maintaining high-quality output.", "questions": "1. Although experiments have shown that fine-tuning is not necessary in multiple task, could generalization tests (such as MRI-PET mentioned in Section 1 Introduction) be provided under different modal combinations for further validation?\n\n2. Although the effectiveness of the loss term has been demonstrated, could authors further ablate the model architecture (such as different encoder or decoder structures in the proposed VAE module architecture, and the detailed architecture design in the proposed fusion prior guidance)?\n\n\n3. Authors currently use visible image as the starting point instead of the other modality or the pure noise. Could authors consideration been given to using other starting point (such as dual modal images) to improve fusion consistency?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strength:\n1. The RFFusion proposed by the author is the first to apply Rectified Flow to image fusion tasks, proposing a one-step sampling mechanism that significantly reduces inference time. \n\n2. A two-stage VAE training strategy is designed for image fusion, effectively solving the problem of inconsistent reconstruction and fusion targets.\n\n3. Quantitative and qualitative comparisons of various image fusion tasks are conducted (infrared visible light, multi exposure, multi focus) and the method performs excellently.\n\n\n\nWeakness:\n1. The pre trained Rectified Flow model is not specifically designed for image fusion, which limits further performance improvement.\n\n2. The author did not conduct ablation experiments based on whether Rectified Flow and VAE structure were used, which limited the evaluation of the improvement points.", "was_revised": true, "final_justification": "Thank you for the rebuttal. It effectively addressed my concerns, and I have decided to maintain my accept score.", "rebuttal": "Thank you for your valuable comments and for acknowledging **the effectiveness of our method across various image fusion tasks**. Below are our responses to your questions, and we hope they address your concerns.\n\n- **W1: The pretrained Rectified Flow model is not specifically designed for image fusion.**\n\nThanks for your comment. We acknowledge that the pretrained Rectified Flow model was not specifically designed for image fusion tasks, and compared to traditional image fusion methods, our approach does not intuitively fuse features from the two source images. However, we would like to emphasize that our method leverages **the powerful generative capability** of Rectified Flow to produce high-fidelity fused images, while incorporating rich information from both images through posterior sampling to ensure the quality of the fusion results. Therefore, the design of our method does not compromise the final performance. Moreover, some approaches utilizing Rectified Flow have also been successfully applied to other low-level vision tasks [1,2,3], which further demonstrates the feasibility of our method.\n\n- **W2: Additional ablation experiments on Rectified Flow and VAE structure.**\n\nWe have added ablation studies on VAE and Rectified Flow. For experiments related to inference speed, please refer to weakness 3 mentioned by **reviewer uZpR**. The quantitative results of the ablation studies are shown in the table. We conducted a comparison on the Roadscene dataset.\n\n| VAE | RF | MI | SF | AG | SSIM |\n|--------|--------|--------|--------|--------|--------|\n| - | ✓ | 3.07 | 12.5 | 5.06 | 0.89\n| ✓  | ✓ | **3.22**  | **14.0** | **5.22** | **0.93**\n\n- **Q1: Providing generalization tests under different modal combinations.**\n\nPlease refer to weakness 2 mentioned by **reviewer fHBh**.\n\n- **Q2: Further ablation of the model architecture.**\n\nWe have added the results of image fusion under different VAE architectures. We conducted experiments using VQ-VAE (with a **downsampling factor of 4** and **without attention**) and trained the VAE structure using the same training procedure.\nThe results are shown in the table below.\n\n| M3FD | MI | VIF | SCD | EN | SSIM | PSNR |\n|-------------|--------|--------|--------|--------|--------|--------\n| VAE (no attention)| 3.08 | 0.73  | 1.57  | 6.68 | 0.89  | 62.4  |\n| VAE | **3.32**  | **0.75**  | **1.60**  | **6.72**  | **0.91**  | **63.2**  | \n\n| RoadScene | MI | VIF | SCD | EN | SSIM | PSNR | \n|-------------|--------|--------|--------|--------|--------|--------|\n| VAE (no attention)| 3.02  | 0.64  | 1.55  | 7.19  | 0.91  | 60.4  | \n| VAE |  **3.22**  | **0.67**  | **1.66**  | **7.28**  | **0.93**  | **61.8** | \n\n| TNO | MI | VIF | SCD | EN | SSIM | PSNR | \n|-------------|--------|--------|--------|--------|--------|--------|\n| VAE (no attention)| 2.63  | 0.62  | 1.57  | 6.66  | 0.88  | 61.8 | \n| VAE | **2.68**  | **0.68**  | **1.61** | **6.99**  | **0.91**  | **62.6**  | \n\nAs shown in the table, although the **VAE without attention** offers a slight advantage in inference speed, its reconstruction capability is inferior to that of the VAE with attention. Therefore, we choose the **attention-based VAE** as our pretrained model.\n\n- **Q3: Other starting points to improve fusion consistency.**\n\nRegarding why we do not use noise as the starting point, you can refer to Question 1 raised by **Reviewer uZpR**. As for using **dual modal images** as the starting point, since Rectified Flow only accepts a single image as input, we perform a simple fusion of the two modality images before conducting experiments. The fusion can be formulated as:\n$$\nI_{\\text{fused}} = \\alpha \\cdot I_1 + (1 - \\alpha) \\cdot I_2.\n$$\nHere, $\\alpha = 0.5$, and our experimental results are shown in the table.\n\n| M3FD | MI | VIF | SCD | EN | SSIM | PSNR |\n|-------------|--------|--------|--------|--------|--------|--------\n| Fused image | 3.29 | **0.78**  | **1.68**  | **6.85** | **0.94**  | **63.4**  |\n| Vis | **3.32**  | 0.75 | 1.60  | 6.72 | 0.91 | 63.2 | \n\n| RoadScene | MI | VIF | SCD | EN | SSIM | PSNR | \n|-------------|--------|--------|--------|--------|--------|--------|\n| Fused image | **3.36** | **0.72**  | **1.74**  | **7.38**  | **0.95**  | **62.7** | \n| Vis | 3.22 | 0.67 | 1.66 | 7.28 | 0.93 | 61.8 | \n\n| TNO | MI | VIF | SCD | EN | SSIM | PSNR | \n|-------------|--------|--------|--------|--------|--------|--------|\n| Fused image| **2.91**  | **0.73**  | **1.71**  | **7.12**  | **0.96**  | **62.8** | \n| Vis | 2.68  | 0.68  | 1.61 | 6.99 | 0.91 | 62.6 |\n\nWe were pleasantly surprised to find that using the fused image as the sampling starting point led to improved performance compared to using the original visible image, as reflected in the evaluation metrics. This demonstrates that our method still holds \n **potential for performance gain** when different images are used as starting points. In future versions of the paper, we plan to further explore the fusion performance when using images containing different proportions of modality information as starting points, in order to fully uncover the potential of our method in image fusion.\n\n**Reference**\n\n[1] Zhu Y, Zhao W, Li A, et al. Flowie: Efficient image enhancement via rectified flow[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024: 13-22.\n\n[2] Li J, Cao J, Guo Y, et al. One diffusion step to real-world super-resolution via flow trajectory distillation[J]. arXiv preprint arXiv:2502.01993, 2025.\n\n[3] You W, Zhang M, Zhang L, et al. Consistency Trajectory Matching for One-Step Generative Super-Resolution[J]. arXiv preprint arXiv:2503.20349, 2025."}, {"review_id": "26uuhQITLq", "reviewer": "Reviewer_eb1j", "summary": "This paper proposes RFfusion, an efficient one-step image fusion framework that integrates Rectified Flow into multimodal fusion tasks. By combining a task-specific latent-space VAE and a two-stage training strategy, the method achieves fast inference and high-quality results across infrared-visible, multi-exposure, and multi-focus fusion benchmarks.", "questions": "Please refer to the weaknesses.", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\n1.Integrating Rectified Flow into multimodal image fusion is interesting and technically reasonable. \n2.The method shows consistently strong performance across multiple fusion tasks.\n3.The paper is well-organized, with clear methodology, and solid experiments.\n4.The proposed two-stage latent space training strategy is practically appealing.\n\nWeaknesses:\n1.The method does not incorporate any explicit mechanisms to model modality-specific characteristics, such as dual-branch encoders or cross-modality attention. Would this design limit the model’s ability to preserve complementary features from each modality?\n2.The VAE encoder exclusively takes the visible image as input, while the infrared modality is introduced only via posterior gradients during sampling. I am curious whether may result in biased latent representations and insufficient encoding of infrared-specific semantics.\n3.Equation (10) introduces a posterior regularization term involving a mapping function, yet the formulation and implementation of M are not clearly specified. It is unclear whether this component is learned, fixed, or task-specific, making the role of the fusion prior difficult to assess.\n4.In Equation (13), the fusion velocity is defined as a sum of the pre-trained rectified flow and a posterior gradient term. However, the dynamics of their interaction are not analyzed. It remains unclear whether this additive formulation can consistently preserve modality alignment or may distort flow trajectories under large posterior corrections.", "was_revised": true, "final_justification": "The authors have sufficiently responded to my concerns. I have therefore raised my score to Accept.", "rebuttal": "Thank you for your valuable comments, especially for recognizing the **technical merits** and **performance of our method**. Below are our responses to your questions.\n\n- **W1: The method does not incorporate any explicit mechanisms to model modality-specific characteristics.**\n\nThanks for your comment. We acknowledge that, compared to our method, dual-branch encoders or cross-modality attention modules offer a more intuitive advantage for image fusion tasks, as they explicitly integrate features from different modalities. However, we would like to emphasize that input images often suffer from quality issues such as noise and blur. Our method leverages the capability of generative models to enhance image quality during the fusion process—something traditional fusion modules are typically unable to achieve. Furthermore, as shown in **Tables 2** and **4** of our paper, our method achieves superior performance in terms of the **MI** metric, indicating that it effectively preserves critical information from the source images even without relying on conventional fusion modules.\n\n- **W2: Indirect infrared input may bias the latent space and weaken infrared feature encoding.**\n\nOur method achieves leading performance in terms of the **MI** metric across multiple datasets. The **MI** metric typically reflects the richness of information from the input images contained in the fused image, which indicates that our method does not suffer from insufficient encoding of infrared-specific semantics.\n\n- **W3: The unclear definition and learning of $M$.**\n\nHere, $M$ can be either a linear or a nonlinear operation. It encompasses the learned, fixed, and task-specific components mentioned by the reviewer. In this paper, we adopt the **Expectation-Maximization** algorithm used in DDFM [1]. However, we do not intend to limit it to a single algorithm. For example, in CCF [2], multiple types of conditions are employed, such as **Basic Conditions**, **Enhanced Conditions**, and **Task-specific Conditions**.\n\n- **W4: The additive fusion in Equation (13) lacks analysis.**\n\nOur formulation defines the fusion velocity as an additive combination of the pretrained Rectified Flow $v_\\theta(f_t)$ and a posterior correction term $\\nabla_{f_t} \\log p(i,v \\mid f_t)$, which serves to guide the generative process toward better alignment with the input modalities. While this additive form may appear simplistic, it is inspired by techniques in **score-based generative modeling**, where posterior guidance is often introduced as a gradient correction to the original flow or score field. Empirically, we found this formulation to be stable during generation, as the pretrained flow already provides a strong data prior, and the posterior term acts as a relatively localized correction based on specific input instances.\n\n**Reference**\n\n[1] Zhao Z, Bai H, Zhu Y, et al. DDFM: denoising diffusion model for multi-modality image fusion[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2023: 8082-8093.\n\n[2] Cao B, Xu X, Zhu P, et al. Conditional controllable image fusion[J]. Advances in Neural Information Processing Systems, 2024, 37: 120311-120335."}]

Author rebuttal:

Meta-review: This paper proposes RFfusion, a novel framework that integrates Rectified Flow and task-specific VAEs for efficient image fusion. The core contribution lies in enabling one-step latent space sampling, combined with a two-stage VAE training strategy, which significantly accelerates inference while maintaining high-quality outputs. Strengths include clear methodological design, strong empirical performance across multiple fusion tasks (infrared-visible, multi-exposure, multi-focus), and inspiring generalization potential for diffusion-based methods. Weaknesses noted by reviewers include limited validation on broader datasets, lack of ablations for individual components, and insufficient exploration of modality-specific representations. However, the rebuttal provided clarifications on VAE training choices and explained fusion dynamics, alleviating major concerns. While additional experiments on medical fusion or other backbones would further strengthen the work, the proposed approach makes a meaningful and timely contribution by demonstrating both speedup and quality gains. Balancing innovation, practical utility, and solid evaluation, I recommend acceptance.
Decision: Accept (poster)