Projection-Manifold Regularized Latent Diffusion for Robust General Image Fusion
Authors: Lei Cao
Hao Zhang
Chunyu Li
Jiayi Ma
Venue: NeurIPS 2025 poster ()
Keywords: Image fusion
general image fusion
multimodal image fusion
latent diffusion models
Primary area: applications
TL;DR: We propose a robust and general image fusion framework that requires no additional training, while effectively adapting to diverse fusion scenarios and effectively addressing various forms of interference.

Abstract: 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 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 superior 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 address 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/Leiii-Cao/PDFuse.

Projection-Manifold Regularized Latent Diffusion for Robust General Image Fusion
Lei Cao', Hao Zhang Chunyu Li', Jiayi Ma
Electronicc Information School, Wuhan University, Wuhan, China Suzhou Institute, Wuhan University, Suzhou, China
whu. cww.ellleeiio..oo..o 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-manifoli 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, develop novel projection-manine regularization, which consists two core mechanisms. On the one hand, the Multi- source Information Consistency Projection (MICP) establishes projection system between diffusion latent representations and source images, solved efficiently via conjugate gradients inject multi-source information into the inference. On the other hand, the Latent Manifold-preservall distribution of diffusion variables with that of the sources, guiding generation respect the model's '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 canonical interference operator set. It synergistically incorporates 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. com The code is publicly available at htttps://gg httpaa/....meeiiiaaa//a/aiaaaeee
Guidance (LMG) aligns the latent
Introduction
Due to hardware restrictions and optical imaging constraints, single-sensor acquisition systems often fail capture scene details comprchensively. Consequently, image fusion technology has emerged to meet the demands of various application scenarios [55). Typical image fusion techniques
include infrared-visible image fusion (IVF) 30,, multi-exposure image fusion (MEF) 47 21
and multi-foche image fusion (MFF) 28 These techniques demonstrate widespread applications in intelligent driving, medical diagnostics, and computational photography [19 (33.
In recent years, the application of advanced feature representations and model architectures to image fusion has enabled task-specific methods achieve encouraging results 31
58 22]. However,
approaches that generalize across multiple tasks [48
67
49 and maintain robustness to degrada- primary challenges. (1) Generality
tion [52] 56, 59 remain relatively scarce. We summarize
challenge: advanced universal image fusion techniques 48 67 primarily depend on custom adapta-
tion networks or memory modules to facilitate cross-task generalization through continual learning.
Corresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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(a) DDFM
Mo
(c) Ours
Me Me-1 Ma-
No
DDPM
S81
0
Xt
x--1
EM
2-
Stuble
LMG
Diffissin
(b) CCF
M
Mo
SØ2
DDPM
S01
MICP
Xx
1-1
S02
V
L X
Zt
Figure 1: Framework diagrams of current training-free image fusion methods based on pre-trained diffusion models. where (a) illustrates the DDFM [65] framework, (b) illustrates the CCF [ framework, and (c) illustrates our proposed PDFuse framework.
However, they require retraining for each task scenario, and in fact, their continual learning networks and memory modules merely average knowledge across scenes, universality remains insufficient. (2) Robustness challenge: current approaches build supervision [56] by creating training pairs of clean and degraded images through degradation removal and addition, but their robustness depends heavily on data availability and typically targets only specific degradation types [57]. Therefore, developing framework that is both robust and truly universal for image fusion remains worth investigating.
By combining diffusion posterior sampling with pretrained diffusion priors [37 16], pretrained latent diffusion models [12 36 have demonstrated strong potential across a wide range domains [44 68), offering new perspective for tackling the generality and robustness challenges in image fusion. However, fusion methods based on pretrained diffusion models remain in their infancy. DDFM (65]
employs EM-based likelihood constraint for multimodal fusion, shown in Fig. [a), while CCF uses a conditional library of fusion metrics to guide the diffusion process, as shown in Fig. [b). Both focus applying DDPM [2] to fusion tasks; however, comprehensive investigations of latent diffusion models with enhanced generative capacities and richer priors are lacking, and neither effectively coordinates multi-source information injection with the utilization of diffusion generative priors to overcome the two major challenges mentioned above.
To overcome the limitations of current approaches, introduce PDFuse, a training-frees universal, and robust image fusion framework that harnesses the generative priors of pre-trained latent diffusion model, shown in Fig. Cc). To our knowledge, PDFuse is the first training-free free image fusion approach built on a pre-trained latent diffusion model. During the generative diffusion posterior sampling process, introduce two key regularization mechanisms efficiently integrate multi-source image information and leverage generative priors. First, the Multi- multi-source Information Consistency Projection (MICP) establishes a projection system between diffusion latents and source images, solved efficiently via conjugate gradients to inject multi-source information and ensure high fidelity. Second, the Latent danifold-preservation Guidance (LMG) aligns the latent distribution of diffusion variables with that of the sources, guiding generation to respect the model's manifold prior and further improve fusion quality. By alternating these two mechanisms, PDFuse strikes an optimal balance between fidelity and generative quality. Moreover, PDFuse constructs a canonical interference operator and integrates it into the dual mechanisms to handle degradations via generative priors without clean-data supervision, significantly enhancing robustness under varied degradation scenarios.
The primary contributions of this study are outlined as follows:
This study proposes PDFuse, the first training-free, universal, and robust image fusion framework built on a pre-trained latent diffusion model, redefining fusion as diffusion inference process constrained by multiple source images.

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We design two regularization mechanisms, the Multi-source Information Consistency Projection and the Latent Manifold preeoeedtreeetttionn Guidance, which are alternately optimized to constrain the diffusion process, enabling PDFuse to adapt to diverse image modalities and generate high-fidelity outputs using the diffusion prior.
We construct canonical interference operator set and embed it into both regularization mechanisms, enabling the framework to handle various degradation issues during fusion without clean-image supervision.
We conducted extensive and comprehensive evaluations of our method. Fusion experiments across multiple task scenarios, along with robustness tests under interference, semantic experiments, and ablation studies, demonstrate the high competitiveness of our approach.
2 Related work
Image fusion. Since the advent of deep learning 55 64
fusion frameworks with learnable parameters such as autoencoders [8]. convolutional neural networks
63 generative adversarial
networks 30 47), Transformers 31 58 and diffusion models (54 51, have dominated image
introduced the first universal fusion framework based information metrics and continual learning, while TC-MoA [67] employs fine tuned adapters for multi task adaptation but remains limited by the performance of adapters and continual learning networks. Text-IF [52] integrates text guidance to address specific scene degradations and Text-DiFuse (56) couples diffusion and fusion networks to improve robustness
fusion. U2Fusion 48
degradation; both methods depend on simulation based degradation data. Although DDFM 655 employs conditional likelihood to leverage pre- pre-trained diffusion models for image fusion, and CCF 3 proposes flexible, controllable condition set, both remain confined to pre-trained DDPMs, lack exploration of latent diffusion models, and cannot synergistically integrate multi- coutie conditional embeddings with generative prior exploitation.
Zero-shot applications of latent diffusion models. The generative capacities and generative diffusion priors of latent diffusion models open new avenues for zero-shot applications in diverse downstream tasks [68] Some methods harness diffusion denoising network attention maps for tasks such as image editing 16]. Meanwhile, adaptive posterior sampling strategies have shown significant promise in areas such as image restoration and protein generation [1). Since image fusion along this trajectory remains in its infancy, exploring training- ffree applications of latent diffusion models in image fusion and fully exploiting their generative diffusion priors is crucial.
Preliminaries
Latent Diffusion models transform data samples 2g (zota(Zo) through forward Itô stochastic differential equation (SDE) [39]
dæ, - --=---300
3() + V (()) dw,,
20 = =t=0:
(I)
where z, E R represents the k-dimensional latent state at time w. is standard k-dimensional
Brownian motion. 3 [0, + R+ defines the noise schedule, and I E R denotes the k-
dimensional identity matrix. For any S < T. the conditional distribution satisfies [12]
2t 2, ~ N (1(1,, 2) E(1, 8R),,
with signal attenuation st = exp ( l 3(w) du and noise accumulation E(t,s) =
S B(u)ee 2 stor du, demonstrating exponential decay characteristics. The corresponding reverse-time SDE, which denoises z7 (0, I&) to 2n ~ Pdatas obeys:
dz, = - - 2., - log))(z)) di + v B(()ddo:
(3)
where ui, denotes reverse-time Brownian motion. A neural network so (2,z 1) approximates the score function Vz, log Pe via denoising score matching (39]. To sample from the posterior distribution p(zal y) x plyl Fo Patt (zo) under observation y, modify the drift term of the reverse-time SDE
3

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Projeclion-Manifoild Regularized Latent Diffusion
Multi-source images
Infrared
DD
Muliiple Stups
PRLLD Step
Mulliple PRID Steps
Ay-A0(0
Zz-2
Sesple
R
Visible
S
5,000"
4
Sempli
LMG mechanism
Oudgut
Figure 2: A diagram depicting the inference architecture of Projection-Manifold Regularized Latent Diffusion shows that each PRLD step comprises an alternating optimization cycle between the MICP mechanism and the LMG mechanism.
incorporate observational constraints
dz 1
1 3())z B(t) so(zi, + v. log p(y)l))) dt + B(t)daw,
(4)
where vz, log p(y|z) derives from - Ve, L under the exponential family assumption p(y (z)(z) e e- The discretized update rule via Euler-Martyama [17] scheme becomes:
Zt = 2, 2.+ At
|
3(1zz (() (so(zz. V=, + (B(I) E~ NO.
(5)
By this approach, no additional training of the diffusion model is necessary to achieve conditional diffusion via posterior sampling.
4 Methodology
Denote the multi-source image inputs as 1t E Hx and t E B and assume pre-trained latent diffusion model consisting of an encoder E(-), decoder D(-), and a latent noise-prediction network sp(-). Following Eq. 4 we reformulate image fusion diffusion inference process constrained by multiple source images. the t-th sampling iteration, two critical mechanisms are integrated: the multi-source information consistency projection mechanism Fo and the latent manifold presoedvatteeeetiioonn guidance mechanism (-M()), defined as:
v Oit-1 Fo (26 (y:))-)) + / ūt- of so so(2,, XPM (2/ (u:)Ez) to,e. (6)
Zt-1
(MICP mechanism)
(LMG mechanism)
Here, the cumulative noise attenuation coefficient is defined as ā. [I' - -B), and the noise modulation term is specified by a. "/ 1-ā.-1 (1 (1- at where 1) [0, 1]. For the 1-a.
{ }" the parameter X governs the strength of the manifold constraint. The clean signal estimate at iteration denoted $ is obtained via Tweedie's formula [12
multi-source image set )
2/1
/1-0; sf) In the following, provide detailed exposition.
vaz
4.1 Alternating optimization with projection strategy and manifold constraints
Multi-source information consistency projection mechanism. formulated in Eq. 6 proposed MICP mechanism embeds multi-source image information via the efficient projection of 2/

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onto the constrained manifold spanned by the multi-source image set (y.) {2"h, }. Inspired by [3]] the fusion constraint loss concisely expressed as follows:
Pusion (D())) Nint (( - max(i", (²") 11 + "grrad VD( max Vem Vi 11.. (7)
where max(-) denotes the element-wise maximum, V represents the gradient operator, and Nint Irad are coefficients balancing the intensity and gradient loss terms. To improve optimization efficiency, we introduce a two-stage projection strategy, as illustrated in Fig. 2 This strategy decomposes the projection into coarse structural component D*()). which captures the overarching shape and form, and fine detail component Dd (=1)), which refines texture and local details, hereafter denoted as D and D respectively.
In the first stage, solve the unconstrained quadratic program pin)
[[
max(i", "00"",,""""""" ."))
which is designed ensure coarse aggregation of the pixel-wise structures from multiple sources. By computing and analyzing the partial derivatives of this objective function, the resulting optimality conditions can be written compactly as the following projection system:
(A*) A D ( (A") where =
(8)
Here, I denotes the identity matrix, and b* { wi"} E ##xWxC,, For instance, in infrared-visible image fusion, te represents the max(-) operator. By employing the conjugate gradient method, we efficiently obtain the coarse structural component D which is produced by the first-stage projection of D(2)).
In contrast to conventional fusion strategies that jointly balance structure and detail, our method explicitly separates texture enhancement as additive refinement layer. During the second stage, building upon the retained coarse structural component D we optimize and add a fine detail compo- nent Dd to recover high-frequency texture information from the multi-source images {y:)²= The detail projection follows two key principles: (i) maximize the preservation of source image textures, and (ii) minimize interference with the previously embedded structural information. Accordingly, by extending the texture loss in Eq. (7), the optimization objective for Dd can be expressed as:
|i(D" + Dd) max(Vi"" V +
w ||
D
min De
Detail constraint
Regularization term
Regalarization term
Here, the detail-constraint term enforces texture consistency with the multi-source images and thus implements principle (i), whereas the first regularizer penalizes excessive modifications the coarse structural component and thereby enforces principle (ii). The second regularizer suppresses rasterization artifacts by penalizing the Laplacian of the detail component. The nonnegative weights and & trade off these objectives to maximize texture fidelity subject to structural preservation. By differentiating Eq. (9) with respect Dd and rearranging the resulting stationarity condition, we obtain the following projection system:
Ada (A
Dd
V b + D
where A о (A*)T
wI
(10)
where 6%_ = (V xz"" V.D*,
b max
V,D'
where V. and V, denote the horizontal and vertical gradient operators, respectively. This projection system can be efficiently solved using the conjugate gradient method.
Owing to the VAE's inherent insensitivity to high-frequency components, we apply Gaussian filter ((.) to the outputs of the projection system as a preprocessing step. The high-frequency residual RR. obtained from this filtering is then added back to the final generated result to restore fine-grained details. Accordingly, the multi-source information consistency projection mechanism can be formulated as follows:
Fo ( ( (E ((1())(1))))) (1) (D* + +D*) āi 26).
(11)
Latent manifold-preservation guidance mechanism. The LMG mechanism is a posterior sam- pling process based on latent diffusion model. By applying the VAE encoder and decoder to both
5

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the multi-source images fv. i and #6, we compute residuals in both the image domain and the latent domain. We backpropagate the corresponding gradients and combine them update and refine 2.. Consequently, the IMG operator can be written as:
PM (E0: (2:12_1) = Vz, Fuston (D(+))) + Matent V2.1| (mma((i"" 1
(12)
Fusion is pixel-domain loss that aligns 2 with the multi-source images {y: 1= This loss
comprises both intensity and texture terms. A complementary term, inspired by [7. serves
latent-space regularizer designed to suppress biases introduced by pixel-space guidance. The scalar
coefficient Mateet controls the strength of this latent regularization.
Alternating optimization. Although the LMG mechanism can guide the latent diffusion process via posterior sampling to embed information from the multi-source image set {3/ } 1. this guidance often incurs significant information loss and degrades the fidelity of the generated images, as discussed in our experiments. Therefore, to balance effective information embedding with latent manifold- preservation guidance, we adopt mechanism and the LMG mechanism. The optimization strategy as follows:
scheme that switches between the MICP
(Fo ( (vr )i=1) O),
if (MICP step).
(Fo(),PM()) =
(13)
($), PM ( (y:
if (LMG step).
4.2 Integrated interference operator set
We assemble canonical interference operator set of N typical operators, [ including overexposure and underexposure, Gaussian blur, motion blur, bicubic downsampling. etc. By integrating the set of degradation operators through both mechanisms, our framework alleviates degradation artifacts in image fusion without requiring paired data to train degraadation-nnversion model. When the degradation types II, and II, are respectively applied to the input images {3/ } referring to Eq. (8). the first-stage projection strategy of the MICP mechanism, after integrating the set of degradation operators, can be expressed as follows:
(Ai)) T An = (Ai) 6".
(14)
Here, II denotes the aggregation of II, and II, and its combination with the corresponding images is related to the fusion weight coefficients at the pixel and texture levels. Building upon Eq. (IO). an upon incorporating the degradation-operatae set, the MICP mechanism's second-stage projection strategy can be succinetly formulated as:
A
O
(A
where A @ (A
WIT II
Cा' (V²) V Dd,
where b max (V
IV
b max V y y IIV D"
(15)
Analogously, the LMG mechanism P. ( {y 1 can formulated as:
PM (z), (m V Vz. Fussion (I()))))) + Natent V || E( (max(i") (n))) ">)) (16)
Here, the operator V (max(i"" (²)) can be represented by the following equation:
v (max(i" (")) II (max(i") ("") + 2/ II H
(17)
Additional implementation details and derivations are provided in Appendix A
5 Experiments
Configuration. We evaluate our method on three representative image fusion tasks: -irrabdd fusion (IVF), multi-exposure fusion (MEF), and multi-focus fusion (MFF). For IVF, 200 test samples from the LLVIP dataset [T5] are used. MEF and MFF are evaluated following the testing setups of the MEFB 62 and MFFW [50] datasets, respectively. In addition, we conducted semantic

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JN
18.088
Visible
Infrared
DDFM
EMMA
TC-MoA
CCF
DCEvO
PDFuse-B
Figure Qualitative comparison of PDFuse-B and state-of-the-ae methods on the LLVIP dataset.
Diseetie
Lowilgtee Modion
Visible
Infrared
MRFS
DDBF
Text-IF
TIMFusion
Text-DiFuse
PDFuse-E
Figure 4: Qualitative comparison the LLVIP dataset under complex degradation scenarios between our enhanced PDFuse-F E and state of the degradation aware image fusion methods.
Table Overall quantitative evaluation of infrared-visible image fusion on the LLVIP dataset using our PDFuse-B and PDFuse-E methods, with the upper subplot showing methods unable to handle degradation and the lower subplot showing degradation sagradation-hhndlingg methods. The red/blue/green
indicates the best, runner-up and third best. Rank is the mean ranking across all metrics.
Method
ET
SFt
ENt
AG1
SD
TEJ
SCDt
Qa+
Rank
U2Fusion (TPAMI
48
28.253
6.628
6.447
2.481
29.286
8.436
0.659
0.455
7.0
DeFusion (ECCV'22)
20
29.604
6.537
7.122
2,513
39.623
7.761
0.705
0.390
6.9
DDFM (ICCV'23) [65
31.124
6.854
7.141
2.649
40.433
7.742
1,081
0.390
EMMA (CVPR (4))
40.653
9.419
7.314
3.561
7.568
0.992
TC-MoA (CVPR'24) [67
42.567
9.805
7.404
3.696
47.623
7.479
1.013
0.422
CCF (NcurIPS'24) [
31.342
7.513
7.045
2.708
39,255
7.838
0.977
0.394
DCEvo (CVPR'25) 25
40.338
9.504
7.346
3.509
46.425
7.537
0.953
0.418
3.9
PDFuse-B (Ours)
48.519
11.028
7.427
4.221
47,931
7.457
0.981
0.437
1.5
DDBF (CVPR'24)
55.108
13,082
7.336
4.806
48,308
7.547
1.021
0.413
Text-IF (CVPR'24 24)
44.917
9.768
7.302
3.844
42.485
7.581
1.001
0.413
MRFS (CVPR') 24)
26.828
6.624
6.719
2.278
36.401
8.164
0.696
0.382
TIMFusion (TPAM)24) 26
34.206
7.363
6.818
2.910
36.079
8.653
0.482
0.397
5.3
Text-DiFuse (NeurIPS'2) 24)
56
38.744
8.711
7.417
3.255
52.255
7.466
1.255
0.403
2.9
PDFuse-F (Ours)
140.302
35.079
7.617
13.634
52.492
7.265
0.824
0.476
1.4
segmentation experiments on the FMB dataset 22 to verify the effectiveness of the fused images generated by our method high-level vision tasks. All experiments ran on an NVIDIA GeForce
RTX 3090 GPU and 2.4 GHz Intel Xeon Silver 4210R CPU. Quantitative evaluation includes
three reference-based metrics: Correlation-Bant Based Quality (Qa)) (29). Sum of the Correlations of
Differences (SCD) 11 and Total Entropy (TE) 224. as well as five no-reference metrics: Edge
Intensity (EI) [R Spatial Frequency (SF) 77. Entropy (EN) 35) Average Gradient (AG) 6,, and
Standard Deviation (SD) (34].

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Under
Over
SAMT.
HSDS.
TC-MoA
CCF
EAT
PDFuse-B
Figure 5: Qualitative comparison of PDFuse-B and state-of-the-art methods on the MEFB dataset.
Evaluation infrared-viste visible fusion. We evaluate approach the LLVIP dataset [T5]
by generating paired clean and degraded test images via degradation removal S and addition operations. To ensure fair comparison,
form experimental groups. In the first group, base model PDFuse-B is compared with four state-of tat-offthhh-nn art general fusion methods (U2Fusion 48
DeFusion 20 TC-MoA [67 and CCF (3)) and three task-specific multi-modal methods (DDFM 65
25 none of which can handle degradation. The qualitative results, encompassing both daytime and nighttime scenarios as demonstrated in Fig. along with quantitative comparisons summarized in Table reveal that our method achieves superior preservation of texture and salient object information. In particular, according to the qualitative analysis, our method exhibits superior infrared salience in pedestrians and effectively preserves fine details in both infrared and visible textures (e.g., bicycle and tree trunk textures), and attains the highest average ranking across all evaluation metrics.
EMMA [66 and DCEvo
In the second group, evaluate enhanced variant, PDFuse-E, which incorporates degradation operator library, against five leading degradation-awaz fusion methods (DDBF [57). MRFS [58], Text-IF [52. TIMFusion [26). Text-DiFuse (56)). Fig- illustrates
method's effectiveness in removing degradation and retaining detail. As shown in the lower half of Table [ PDFuse-E outperforms current state-of-the-ar techniques texture, contrast, and structural metrics and secures the highest Rank.
Table 2: Quantitative comparison multi-exposure image fusion methods on the MEFB dataset. The red/blue/green denotes first, second and third best. Rank is the mean ranking across all metrics.
Method
EI1
SFT
ENt
AG1
SD
TEJ
SCD
Qat
Rankl
U2Fusion (TPAMI'22)
48
49.381
14.503
6.767
4.393
46.789
5.185
5.515
0.446
6.9
DeFusion (EC
20
52.704
15.319
6.799
4.570
52.734
5.150
0.275
0.370
6.5
HSDS-MEF (AAAI'24)
45
64.217
19.379
7.136
5.951
55.440
4,813
0.859
0.449
2.4
TC-MoA (CVPR 24) [67
56.439
15.782
7.093
5.049
49.852
4.856
0.538
0.450
4.4
CCF (NeurlPS'24) [3]
51.933
17.501
7.134
4.855
60.174
4.815
1.163
0.406
3,8
SAMT-MEF (InF"24)
14
55.097
17.518
7.046
5.146
50.824
4,904
0.635
0.450
3.9
EAT (TMM'25)
42
45.237
13.366
6.916
4.055
47.096
5.033
0.423
0.428
7.0
PDFuse-B (Ours)
69.772
20.695
7.271
6.316
60.463
4.678
0.774
0.457
1.3
Evaluation on multi-exposure fusion. We compare our method, PDFuse-B, against four general- purpose image fusion techniques (U2Fusion 48). DeFusion (20]. TC-MoA (67), and CCF 3 and three task specific multi celti--expsure fusion methods (SAMT-MEF 14, HSDS-MEF 45 and EAT as competitors. As shown in Fig. 5] our approach delivers the most consistent global exposure and color while offering clear advantages in texture, contrast, and color saturation. Our method shows significant advantage in overall exposure levels and color contrast compared to other methods, and the highlighted regions further confirm its superiority preserving textures across images with different exposures (e.g., windows, posters, clouds). The superior quantitative results in Table further confirm these improvements.
Evaluation on multi-focus fusion. addition to the four general-puroose fusion methods, we compare our approach with two leading multi-focus fusion techniques, ZMFF [13 and DB-MFIF (0) Fig. 6 demonstrates that our method more effectively preserves texture and combines details from images with different focus regions. In particular, approach preserves textures across varying

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Table Quantitative comparison of multi-focus image fusion methods on the MFFW dataset. The
red/blue/green denotes first, second and third best. Rank is the mean ranking across all metrics.
Method
Elt
SFT
ENT
AG1
SD1
TEL
SCD
Qat
Rank
U2Fusion (TP 20
72.763
17.152
6,953
6.595
48.967
7.312
0.465
0.586
5.8
DeFusion (ECCV'22) 20
57.015
12.937
7.120
5.010
51.224
7.146
0.410
0.556
6.5
ZMFF (In)))33 [33
85.799
22.175
7.188
8.026
53,682
7.077
0.500
0.671
3.4
TC-MoA (CVPR'24) 67
74.781
18,620
7.184
6.805
54.094
7.081
0.719
0,650
4.0
CCF (NeurIPS'24) 24) [
65.660
16.613
7.261
5,998
59.329
7.004
1.330
0.539
DB-MFIF (TMM'24)
87.087
24.045
7.221
8.321
55.158
7.045
0.677
0.662
2.6
PDFuse-B (Ours)
93.756
22.523
7.278
8,463
60.445
6.990
0.894
0.576
Source1
Source2
DeFusion
ZMFF
DB-MFIF
TC-MoA
CCF
PDFuse-B
Figure 6: Qualitative comparison of PDFuse-B B and state-of-the methods on the MFFW dataset.
Bachgrandd
Mattrmycle
Building
Visible
TC-MoA
MRRS
Visible
Tec- P
Tc-MAA
MES
IMM:
Iect-DL
OCEND
Teat
Infrared
Infrared
PBRUse-
PORuse-E
Figure 7: Qualitative comparison of semantic segmentation performance on the FMB dataset.
Visible
Infrared
III
IV
PDFuse-B
PDFuse-E
Figure 8: A qualitative comparison for the ablation study on the LLVIP dataset.
focus depths while maintaining high overall image contrast. When compared the fiffussin-based CCF 31 our method achieves markedly higher texture fidelity. These qualitative advantages are corroborated by the quantitative results in Table which show superior performance across multiple multi-focus fusion metries.
Semantic verification on segmentation. We retrained SegNeXt-B S on the fusion outputs of all methods from the FMB training and evaluated the corresponding test set (22); qualitative comparisons are presented in Fig. 7 and quantitative results in Table 4 where our enhanced method PDFuse-E attains the highest segmentation mloU and mAec.
Ablation studies. We performed an ablation study on the LLVIP dataset to assess the impact of each core component in our framework, including (1) the LMG mechanism, (II) the first projection stage of the MICP mechanism, (III) the second projection stage of the MICP mechanism, and (IV)

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Table Quantitative comparison of semantic segmentation performance on fused images from the FMB dataset. The red/blue/green indicates the best, runner-up up and third best.
Method
Person
SKy
Lamp
Car
Bus
Truck
Motor
Pole
mAcc
mloU
DDBF
62.97
93.73
33.11
81.60
35.00
49.38
45.81
46.61
68.12
59.89
EMMA
68.27
93.47
44.43
81.71
34.87
47.18
49.55
47.92
70.69
61.87
Text-IF
67.08
94.04
40.00
81.77
28.00
48,48
47,47
48.52
69.54
61.15
TC-MoA
67.34
94.11
43.02
80.90
41.29
43.15
44.61
47.31
69.69
61.67
MRFS
68.37
94.09
36.95
84.02
56.55
49.20
46.27
47.12
71.16
63.00
TIMFusion
66.04
93.72
41.43
83.62
50.71
46.07
44.41
45.70
70.72
62.25
Text-DiFuse
67.79
94.12
37.44
83.04
62.96
39.16
45.24
46.56
70.98
62.61
DCEvo
69.44
93.81
41.42
83.09
62.65
28.71
43.54
45.45
70.89
62.53
PDFuse-B
69.06
94.07
39.23
82.22
43.77
45.30
49.72
48.25
70.97
62.54
PDFuse-E
66.88
93.80
45.51
83.83
57.99
48.15
47.09
47.65
71.77
63.70
Table 5: Quantitative evaluation of the ablation experiments on the LLVIP dataset.
Method
MICP
LMG.
Opr.
EIf
SF1
ENt
AG1
SDT
TE.
SCD
Q+t
X
X
45.26
10.01
7.394
3.941
46.71
7.489
0.975
0.443
X/st.1 X/st.2
X
47.72
10.59
7.408
4.132
47.36
7.475
0.987
0.432
III
X
42.54
10.06
7.385
3.661
47.11
7.498
0.901
0.406
IV
X
x
33.76
8.413
7.155
2.892
40.85
7.718
0.678
0.316
PDFuse-B
V
X
48.52
11.03
7.427
4.221
47.93
7.457
0.981
0.437
PDFuse-E
V
140.3
35.08
7.617
13.63
52.49
7.265
0.824
0.476
the entire MICP mechanism; qualitative results are presented in Fig. and quantitative comparisons reported in T
The results reveal that removing the first projection stage of MICP causes color distortion because this stage injects the coarse structural component DD' into the diffusion process, and without it low- frequency and color information cannot be effectively preserved. Removing the second projection stage leads to significant texture loss, since it injects the fine-detail component D When the entire MICP mechanism removed, relying solely on the LMG mechanism fails to maintain information fidelity and results in poor fusion quality. Conversely, if the LMG mechanism is removed while MICP remains, the MICP mechanism still supports structural and texture preservation, but the fusion exhibits reduced contrast and darker tones compared to PDFuse-B (which includes LMG), due to the lack of manifold preservvttotss guidance and consequently limited use generative priors.
Finally, PDFuse-E, which integrates the interference operator set, outperforms PDFuse-B by lever- aging diffusion priors address inverse degradation problems (e.g.. low light and blur), thereby producing better overall visual quality. These findings demonstrate that the full configuration delivers the best overall performance.
6
Conclusion
This paper proposes PDFuse, robust, training-free free image fusion framework built on pre-trained latent diffusion models with projection manifold regularization. PDFuse formulates fusion multi-source regularized latent diffusion process and employs two complementary regularization mechanisms to ensure high fidelity and effective use of diffusion priors. It also integrates canonical interference operator set into these mechanisms, allowing the model address diverse degradations without clear-image supervision. Extensive experiments demonstrate that PDFuse outperforms current methods in both fusion quality and preservation of semantic attributes.
dntection-maaifoldd
Acknowledgement
This work was supported by the NSFC (62506268, 62276192) and the Natural Science Foundation of Jiangsu Province (BK20250454).
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21

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Appendix
A
More details of PDFuse
To further enhance the clarity and completeness of our discussion, we provide additional architectural details of the proposed PDFuse framework.
A.1 More details of the MICP mechanism
The fine detail projection. The Sobel operator V used for extracting texture features can be decomposed into horizontal gradient operator V and vertical gradient operator V y. Accordingly, the detail-constraint term in Eq. (7) can be reformulated
min Dd
V + DD) - max (Z
VD + D4) max (V
y
detail-constraint term
(18)
Taking the derivative of Eq. with respect to Dd yields:
OC
0Dd = 2 V (Dx - Ta)
(D' - -T,) + wDd + (V (V² yD (19)
#c Specifically, we define T- max , 7 V v= and Ty = max v i v, By setting and rearranging the terms, we obtain:
x V2 + V, + wI of o(V2)
(V VI V V (T VyD"). (20)
This result is consistent with Eq. 10)) in the main text.
Conjugate gradient method. The two-stage projection system is fully solvable via the Conjugate Gradient (CG) method. In the first-stage projection, the coefficient matrix reduces to the identity operator I, which is trivially symmetric positive definite (SPD) since (xx Ix x 0 for all x * O. For the second stage projection with composite operator:
A
(A
V
wI regularization
(21)
gradient terms
high-order
is solvable via the Conjugate Gradient (CG) method 38 since Ad O (A satisfies the required
symmetry and positive definiteness. Symmetry arises from: 1) Adjoint Sobel operators V V and V = V 2) Self-adjoint Laplacian 2
V.V. + V Vy, y. and 3) Explicit symmetry of wI.
Positive definitnnsss follows from:
(x. A (A
3
w 1
V
O, Va + O,
(22)
where the strict inequality holds due the wI term. The CG method therefore guarantees convergence to the unique solution within finite iterations.
A.2 More details of alternating optimization
We collected series of intermediate diffusion results under the ittllarrizztiiionnnnrtrnnn diffusion process, D( which allows for an intuitive visualization of the effects imposed by different regularization mechanisms, demonstrated in Fig. From these results, it is evident that the MICP mechanism produces a pronounced enhancement of multi-source information, whereas the LMG mechanism yields an overall improvement in visual quality.
A.3
More details of interference operator set
The interference operator set (II, J defined as library degradation operators, which includes a collection of representative interference models. These operators are integrated into the bidirectional inference mechanism our framework to address potential disturbances that may arise during the fusion process.
22

---

MICP Step
LMG Step
MICP Step
LMG Step
Noise sample
Output
Iaputs
Figure An intuitive visualization of the effects imposed by different regularization mechanisms.
Implementation details of representative degradation operators. For degradations caused by low-light conditions and overexposure, we adopt the illumination decomposition theory based on the Retinex model, where the observed image is represented as j = /L. To estimate the degraded illumination component, we follow the approach proposed in [9]. Specifically, for handling both low- light and overexposed regions, we leverage forward and inverse illumination estimation strategies as described in [6]]. The resulting illumination map J. is then incorporated into framework guide the optimization of illuiination-wwr fusion. Other degradation types, including Gaussian blur, motion blur, and thermal diffusion in infrared images, are implemented via convolutional operations. By constructing both the degradation operators and their corresponding transpose (adjoint) operators, these degradation processes can be universally integrated into our proposed bidirectional inference mechanism, thereby assisting the diffusion- -baee reasoning in a more robust manner.
Integrated strategy. Given set of multi-source image inputs ) when each image is subject to different degradations II; and II, drawn from a predefined degradation operator set (II, J.V. directly applying the basic projection-maniee inheriting the degradations from the source images, which is an undesirable outcome. Therefore, we further revise the formulation in Eq. 7 as follows:
regularization inevitably results in the fused image
Crusion (T)()))) = Tini (T))(E)) max(i"). .>l Igra V (IID(E)) max (Vim,
In this context, II denotes weighted combination of II, and I, where the weighting coefficients are determined by the fusion contributions of the respective source images. When the source images undergo different types degradation, the consistency loss between II and the multi-source images is established by treating II as fused operator derived from II, and II, The fusion coefficients are determined by the spatial weights of the multi mouti-source images during each stage of regularization.
Taking the first-stage projection of the MICP mechanism in the VIS task as an example, in order to preserve the saliency of the infrared image and the majority of scene details from the visible image, the fusion is typically performed using the max(-) operator. In this case, the spatial fusion weights coeff,,, and coeff,, satisfy the relation:
coeff + coeff, 6"h = max
(24)
Accordingly, the fusion of the interference operator sets II, and II, also follows this coefficient scheme. In the second stage of the MICP mechanism, the fusion coefficients for the interference operator sets are similarly derived based on the fusion weights computed over Sobel gradients. Each component of the LMG mechanism adheres to similar principles. Based on the same derivation process described earlier, the resulting projection-manifoe regularization formulations correspond to Eq.
Eq. (15) and Eq. (16).
B Explanation on VAE fidelity
VAE does exhibit significant information bias and loss when applied to high-quality visible images with very dense and regular textures. However, the loss introduced by VAE is typically concentrated its high-frequency components. We perform 2D FFT on both the original image and the VAE- encoded and decoded reconstructed image, generating a radial frequency grid followed by frequency band partitioning, which is used to study the distribution of MSE losses from VAE encoding and decoding across different normalized frequency bands the LLVIP dataset, as shown in Table 6
23

---

Table 6: The distribution MSE losses from VAE encoding and decoding across different normalized frequency bands on the LLVIP dataset.
Frequency Range (cycles/pixel) MSE Loss
0.00-0.025
0.025-0.050
0.050-0.100
0.100-0.250
0.250-0.500
0.000000
0.000001
0,000003
0.000062
0.001003
is clear that VAE's loss and reconstruction bias are concentrated in the high frequencies. To address this challenge, we adopted high-frequency residual injection strategy. Specifically, in MICP mechanism, the regularized projection captures the coarse structural component 2 and the fine detail component Dd from the multi-source image set i" 1 Then, seherrrr filter G(-) is applied to remove the high-frequency components from the projection result as in Eq. (TT). other words, the high-frequency information is kept R rather than encoded into the latent space. Moreover, R is iteratively refined via projection regularization of 2 and finally injected as residual after the process completes. Additionally, we further conduct an ablation study on the high- high-frequency filtering and residual structure. The MICP mechanism projection result is directly encoded by VAE into the latent space (without pre-reserving high-frequency information), and the quantitative comparison is shown in T
Table 7: Ablation comparison of -ree-freueecy information skip residual injection.
Methods
Elf
SFi
AG1
ENT
SD1
TET
SCD1
Qat
wo / (filtering and residual)
39.391 48.519
8.070
7.359
3.371
45.077
7.528
0.913
0.418
PDFuse
11.028
7,427
4.221
47.931
7.457
0.981
0.437
Based on the ablation results, the model variant lacking high-frequency filtering and residual compo- nents exhibits significant loss in edge and texture details. These results demonstrate the effectiveness of our adopted high- fegh-freuuency residual injection strategy.
C Algorithm flowchart
This figure presents the algorithmic flowehart of our proposed method, the crojection-manifee regularized latent diffusion framework. The flowehart divided into two variants: the basic version (PDFuse-B) and the enhanced version (PDFuse-E).
Algorithm 1 PDFuse-B
Algorithm 2 PDFusc-E
Require: T, [a D. im 1: Sample zT I)
Require: T, [T )T D, 2m
1: Sample 2T ~ ~N
2: for -o'to do
for To
3: En < -50(z,, c)
3: ég s0(2,, 1,)
4:
2 --vI-oa
4:
$1 <
x-vI-o, vo:
va,
5:
if MICP step then
5: if MICP step then
6:
D - Eq. (8)*
D Eq. (14)²
7:
Dd + Eq. (10)f
7:
Dd - Eq. (15)
8:
R+ (I(I (D" D
(D R (I- Dd)
9:
Fo(E/, {y:}2_)
< Eq. (11)²
Fo {y:}4_)
Eq. (11)*
PM (vi)²_) +
10:
PM (2) (1r)2
11:
2:-1 < Eq. (6)
11:
2;-1 +- Eq. (6)
12:
else if LGM step then
12:
else LGM step then
13:
Fo Fo 22) {y ) t 2
13:
Fe F {yr (2 + //
14:
PM 2/ (y:)2=)) + Eq. (12)*
14:
PM (+) (y:)2 + Eq. (16)*
end if
15:
end if
16:
end for
16:
end for
return D(21-1) R
17: return D(z,-1) +
24

---

D Support image fusion with more than two source images
Since our method formulates the fusion process as a regularized latent diffusion conditioned on multi-source images, it does not impose an explicit limit on the number of inputs. Conse- quently, our approach can process an arbitrary number of source images in single pass with negligible additional computational or temporal overhead. We validated this capability on the SICE multi-exposure dataset [2] by inputting three images with different exposure levels. It is worth emphasizing that other methods can only process two images at time, so they perform multi-exposure fusion of three images in two separate stages via sequential pairing. In contrast, our method achieves this in a single pass. The quantitative results are presented in Table g From the experimental results, our ap- proach achieves leading performance on most evaluation metrics, demon- strating that our regularization mecha- nism can be generally applied to fuse three or more source images. In ad- dition, we further analyzed how the runtime and the Edge Intensity (EI) of the fused images produced by method, PDFuse, change as the num- ber of input images with different posure settings varies. The quantita- tive results are presented in Table
Table 8: Quantitative comparison of malti-exposure image fu- sion methods with 3 exposure inputs on the SICE dataset [2]. The red/blue/green denotes first, second and third best.
Methods
E11
SFt
END
AG1
TEL
SCD)
Qu
U2Fasion
85.165
22.561
7.225
8.048
54.565
13.255
0.716
0.500
DeFusion
67.214
17.257
7.279
6,206
$2.841
13.201
0,550
0.357
HSDS-MEF
95.637
29.464
6.994
9.367
73.821
13.486
2.989
0.351
TC-MaA
89.575
24.851
7,532
8.611
57.462
12.947
0.959
0.466
CCF
86.120
28.723
7.614
8.885
70.408
12,866
3.575
0.461
SAMT-MEF
89.485
29.514
7.425
9.378
58.071
13.055
1.225
0.430
EAT
36.849
9.384
6.501
3.146
40.089
13.937
1.700
0.347
PDFuse-B
112.422
30.975
7.708
10.930
68.581
12.771
2.349
0.489
Table 9: Comparison of Edge Intensity (EI) and Runtime versus the Number of Input Exposure Sequences on the SICE Dataset.
Input Sequence Counts
2
3
4
5
6
7
Edge Intensity (EI)
95.155
112.422
117.805
119.518 8
121.840
121.855
123.009
124.45 18.23
Runtime (s)
18.08
18.15
18.03
18.24
18.33
18.19
18.30
It can be observed that, for the same scene, as the number of input images with different exposure settings increases, the scene information texture (EI) of the fused images gradually improves, while the runtime remains essentially unchanged.
E More comprehensive experimental evaluations
To conduct more extensive experimental evaluations and further demonstrate our method's generality, we carried additional assessments on larger benchmark datasets for infrared-visible image fusion, multi-exposure image fusion, and multi-focus image fusion.
E.1 More evaluations for infrared-visibe fusion
We conducted additional evaluations four widely used infrared-visible fusion benchmarks: the TNO, FMB [23], MSRS [40). and RoadScene [46] datasets. Two experimental groups were estab- lished. In the first group, base model (PDFuse-Bi B) was compared with current sfateof-the-att fusion methods. In the second group, our enhanced model (PDFuse-F). which integrates degradation operators, was evaluated against leading fusion methods capable of handling degraded inputs. The TNO dataset comprises 42 image pairs from diverse scenes; MSRS employs a 300-image test set; and RoadScene and FMB were each assessed on randomly sampled subsets of 200 and 150 image pairs,
respectively. Quantitative results for these four datasets are shown in Fig. 14 Fig. 15] Fig. [6 and Fig. 17 PDFuse-E consistently improves target saliency, preserves fine texture details, and enhances overall visual quality. Furthermore, numerical comparisons in Table IT and Table TO demonstrate that both PDFuse-B and PDFuse-E achieve the highest mean scores across all evaluated metrics.
E.2 More evaluations for multi-exposure image fusion
25

---

Table 10: Overall quantitative evaluation of infrared visible image fusion on the MSRS dataset and RoadScene Dataset using our PDFuse-B B and PDFuse-E methods, with the upper subplot showing methods unable to handle degradation and the lower subplot showing pngradation-handiing methods. The red/ red/blue/green indicates the best, runner-up and third best. Rank is the mean ranking across all metries.
MSRS Dataset
RoadScene Dataset
Method
E1'
SFT
ENT
AGT
SDT
TE1
ERT
SF"
ENT
AG1
SDT
TE.
Rank]
U2Fusion (TPAMI'22)
23,306
6,838
2,093
19.753
6.799
56,238
11,783
6.843
4,801
32.461
7,438
7.3
DeFusion (ECCV"22)
28.744
7.928
6.355
2.535
36.603
5.362
42,430
8.607
6.898
3.390
36.378
7.383
6.4
DDFM (IOCVV23)
25.981
6.942
6.074
2,330
28.055
5.644
51,476
11,555
7.189
4.494
41.711
7.093
6.1
EMMA (CVPR 24)
38.692
10.818
6.614
3.462
42.959
5.104
72.728
16.376
7.446
6.298
56,223
6.835
2.0
TC-MoA (CVPR
39,342
10.875
3.562
55.
5.118
600,408
12.089
7.142
4.973
40.554
7.139
CCF (NcurIPS:24)
27.831
8,20]
6.063
2.568
27.248
5.655
53-121
13.076
7.291
1.640
46.030
6.990
5.0
DCEvo (CVPR 25)
38.405
10.808
6.519
3.451
41.519
5.199
65.582
14.940
7.139
5.553
44.560
7.142
4.0
PDFuse-B (Ouns)
44,223
12.630
6.703
3,959
43.429
5.014
76.644
16.670
7.210
6.535
46.422
7.071
DDBF (C4)
$1.530
13.959
6,945
4,523
51.864
4.772
45.102
10.307
5,834
3,521
23.518
8.447
4.4
Text-IF (CVPR'24)
38.081
9,473
3.332
4.750
MRES (CVPRR (4)
27.905
8,456
6.411
2.405
37.081
5.307
51,052
11.096
7.386
4.226
53.377
6.8%6
4.2
TIMF usion (IPAMI224) 24)
41.059
10.943
6.957
3,567
12. 832
1.760
47.159
10,562
6.994
3.879
40.723
7.287
4.0
Disse (NeurIPS'2) 24)
39.538
10.740
7.082
3.413
52.621
4.635
45.382
9.982
7.310
3.714
50.807
6.971
3.4
PDFuse-E.(Om)
83.699
20.623
7.363
7.484
49.770
4.355
95.914
22.383
7,238
8.359
47.386
7.043
Table 11: Overall quantitative evaluation of -iirared-visible image fusion on the TNO dataset and
FMB Dataset using our PDFuse-B and PDFuse-E methods, with the upper subplot showing methods unable to handle degradation and the lower subplot showing degradation handling methods. The
red/blue/green indicates the best, runner-up and third best. Rank is the mean ranking across all
metrics.
TNO Dataset
FMB Dutuset
Method
EI'
SF1
EN1
AG
SD
TE.
E1:
SF-
EN1
AG
SD1
TEL
Runk]
U2Fusion (T
36.194
7.799
6.292
3.245
23.687
6.862
36.109
10.196
6.590
3.221
28.391
6.605
6.4
DeFusion (22
28.289
5.775
6.461
2,365
29.091
6,693
26.921
7.777
6.338
2,300
25.656
6.857
7.8
DDFM (ICCV223)
37.096
8.665
3.421
6.283
27.841
EMMA (CVPR 24))
51.375
11.375
7.149
4.694
45.575
6.004
46.795
14.383
6.747
4.201
34.037
6.448
1.8
TC-MoA (CVPR'24)
41.128
9,250
6.777
3,613
34.397
6.377
44.411
14,045
6,756
3.974
32.412
6.439
3.5
CCF
(NeurIPS:24) 24)
36.462
9.448
6.804
3.448
34.708
6.350
27.560
9.029
6.452
2.467
26.145
6.743
6.0
DCC (CVPR'25)
41.786
6.767
3,752
36.215
6.387
42,49%
13.215
6.647
3.273
34,042
6.498
3.8
PDFuse-B (Ouns)
52,487
12.116
7.025
4.787
40,033
6.129
47,885
14.440
6,713
4.242
34,401
6.482
1.6
DDBF (CVPP'22)
52,946
14.652
4,965
35.384
6.760
44.657
15,434
5.627
3.842
7.568
Text-IF (CVPR'20 24)
48.113
11.547
7.051
4.480
43.358
6.103
47.461
15.063
6.660
4.235
31.488
6.535
3.2
MRFS (CVPRR244
39,513
9,223
7.174
3.549
45.977
5.979
36,958
11,831
6.740
3,257
34,313
6.456
3.4
(TPAMI224)
41.144
9.749
44.298
13.396
Text-DiFuse (NeurIPS22) 24)
32,669
7,230
6.991
2.738
47.074
6.162
36.024
11.676
6.920
3.162
37.415
6.275
4.1
PDFuse-E (Ours)
79.869
18,300
7.079
7.499
40.303
6.075
72.684
21.307
6,349
6.557
32.504
6.646
2.5
We randomly selected 200 images from the SICE dataset (2 for further evaluation. The qualitative results are presented in Fig. [8] while the quan- titative findings are summarized in Table 12] Our fusion method out- performs current tfate-of-the-ar art ap- proaches in overall exposure balance, color fidelity, and detail preservation, and also demonstrates superior perfor- mance across the aggregate evaluation metrics. To enhance the comparison comprchensiveness, we additionally introduce MEF-SSIM 322 for evaluation. MEF-SSIM is a
Table 12: Quantitative comparison of multi-exposure image fusion methods the SICE dataset 2]. The red/blue/gee denotes first, second and third best. Rank is the mean ranking across all metrics.
Methods
SF1
ENI
AG1
SD1
TE.
SCD:
Q+1
Rank!
U2Fusion
77.570
21.379
7.119
7.341
50.364
5.461
0.799
0.475
4.5
DeFusion
73.184
20.298
7.102
6.884
50.023
5.478
0.030
0.426
6.4
HSDS-MER
68.284
24.476
7.262
8.278
51.805
5,318
0.725
0.477
3.5
TC-MoA
69.101
17.624
7.154
6.436
45.080
5.426
0.423
0.485
5.8
CCF
74.602
24.061
7.328
7.478
56.988
5.252
1.186
0.461
3.1
SAMT-MEF
76.682
24.253
7.205
7,811
47.788
5.375
0.612
0.486
3.6
EAT
61.909
19.253
7.063
6. 6.245
43.476
5.517
0.345
0.426
7.6
PDFuse-B
90.641
26.568
7.461
8.828
58.199
5.119
0.877
0.482
1.4
structural similarity evaluation metric specifically designed for MEF, which has been widely adopted and recognized in the MEF field. This metric performs perception-driven quality assessment by measuring the consistency between the fusion result and the ideal structures of multi-source exposure images. The quantitative experimental results are shown in Table L3
E.3 More evaluations for multi-focus image fusion
26

---

Table 13: Comparison with advanced methods based on the MEF-SSIM metric on the SICE dataset.
Methods U2Fusion
DeFusion
HSDS-MEF
TC-MoA
CCF
SAMT
EAT
PDFuse
MEF-SSIM
0.8030
0.8297
0.8164
0.8838
0.7963
0.8617
0.8574
0.8856
We further present a comprehensive evaluation of multi-focus image fu- sion performance the widely-used Lytro dataset [27). with qualitative re- sults in Fig. and quantitative results in Table
Table 14: Quantitative comparison of multi-focus image fusion methods on the Lytro dataset. The red/blue/green denotes first, second and third best. Rank is the mean ranking across all metrics.
Our method not only demonstrates superior performance over the peer CCF approach but also outperforms all other competing algo- rithms, achieving the highest average ranking across every quantitative met- ric.
Methods
ETT
SFT
ENT
AG
SDT
TE
SCD1
Qa1
Rank
U2Fusion 62.670
14.890
7.304
5.589
51.853
7.673
0.373
0.648
5.8
DeFusion
48.944
11.117
7,468
4.421
54,483
7.509
0.330
0.597
6.6
7.525
56.800
DE-MFIF
75.006
19.641
7.537
6.895
57.654
7.439
0.576
0.778
3.1
CCF
53.997
13.526
7.592
4.767
62.389
7.384
1.338
0.598
3.8
PDFuse-B
81.497
19.854
7.613
7.266
61.799
7.364
0.818
0.666
1.8
F Extended application
E1 Evaluations for medical multimodal image fusion
To further validate the generality of our approach, we performed extended evaluations in the context of medi- cal multimodal image fusion. Specif- ically, we employed the CT-MRI fusion subset of the Harvard Med- ical School dataset as representa- tive benchmark. Our method was compared against both state-of-the- art multimodal fusion algorithms and image fu-
Table 15:
Quantitative comparison of medical image fusion methods on the Havard medicine dataset. The red/blue/green denotes first, second and third best. Rank is the mean ranking across all metrics.
Methods
ETT
SFT
ENT
AG
SDT
TEJ
SCD1
Qa?
Rank
U2Fusion
50.292
19.931
4.335
4.762
73.959
1.443
1.015
0.550
5.1
Del DeFusion
56.435
19.537
4.497
5.321
52.312
1.282
0.542
0.284
5.6
DDFM
46.170
19.226
4,038
4,465
63,580
1.740
0.995
0,574
6.0
EMMA
67.381
23.162
4,995
6.449
82.784
0.783
1.409
0.494
TC-MoA
57.787
25.956
4.480
5.668
74.192
1.298
1.242
0.291
4.3
CCF
64.751
87.586
widely used general-purpee sion techniques. Qualitative comparisons are presented in Fig. 20 and quantitative results are sum
PDFuse-B
76.292
32.205
4.540
7.279
90.330
1,238
1.516
0.503
1.7
marized in Table [5] The results demonstrate that the proposed approach not only preserves critical anatomical structures with high fidelity but also accurately reflects regional variations in metabolic activity.
F.2 Evaluations for complex weather degradation scenarios
We extracted challenging complex- weather degradation scenes from the M3FD infrared-visible image fusion dataset, which was captured using a dual-lens optical camera and a dual-lens infrared sensor. This dataset contains adverse environmental conditions such haze combined with low light, low light accompanied by headlight glare. and haze mixed with noise. To evaluate our proposed PDFuse-E method, we compared it with two cascaded schemes. the first scheme, the visible image was pre-enhanced using the DA-RCOT restoration method 4 before being fused with DCEvo. In the second scheme, the visible image was pre-enhanced using the OneRestore method [10] and then fused with DCEvo. The quantitative comparison results are presented in Table I6
Table 16: Quantitative comparison on challenging complex- weather degradation scenes from the M3FD dataset.
Methods
Elt
SFt
AG1
ENt
SD
TE
SCD1
Qat
DA-RCOT DCEVo
67.56
17.94
6.31
6.78
32.71
6.89
1.57
0.51
OneRestore + DCEvo
105.45
27.90
9.86
7.14
42.20
6.53
0.96
0.45
PDFuse-E
96.07
24.54
9.08
6.88
35.14
6.79
1.35
0.49
27

---

From the table, we observe that the OneRestore + DCEvo method achieves the best performance on several non-reference metries such as EI, SF, and AG, but yields relatively low scores on full-reference metrics like SCD and Qcb. In contrast, the DA-RCOT + DCEvo method performs best on SCD and Qcb, yet falls behind on non-reference metries such as El and SF. While our method does not outperform the others any single metric, it achieves more balanced overall performance. For example, our scores on non-reference metrics like EI and AG are very close to those of OneRestore + DCEvo and significantly higher than those of DA-RCOT DCEvo. At the same time, our full- reference scores on SCD and Qcb remain relatively high.
From qualitative perspective as illustrated in Fig. [0 in low-light and extreme haze- sezzedeg scenes, both OneRestore and DA-RCOT struggle restore visibility in the hazy regions of the visible image, resulting in fused outputs that retain heavy haze and hinder the integration of infrared information. While our PDFuse-F method also cannot directly restore haze the visible image, the interference operator set adjusts the global exposure structure, effectively suppressing haze brightness and thus reducing its fusion weight. This enables the fused output to preserve rich infrared texture information in those regions.
Visible
Infrared
MA-RCOT-DCEM
OneRestore DiRRestore-DDEEv
PDFuse-E
Figure 10: Qualitative comparison under complex weather degradations.
G
Limitation
Although our method the first training-free, general, and robust image fusion framework based on pretrained latent diffusion models and achieves good performance across multiple tasks, it also shares common limitation with most diffusion model methods, namely, relatively low runtime efficiency. In Table 17 we provide the memory usage and runtime efficiency of various methods. Regarding runtime efficiency, several diffusion model methods perform poorly. Although our method runs faster compared to similar approaches like CCF and DDFM, there remains significant room for improvement compared to other architectural methods.
Table 17: Statistical results of parameters and runtime.
Methods
U2Fusion
DeFusion
TC-MoA
EMMA
DCEvo
DDFM
CCF
PDFuse-B
PDF PDFuse-E
Memory usage/M
2.51
30.04
1299.23
5.79
7.65
2108.82
2108.82
2033.68
2033.68
Runtime/Second
1.14
0.74
57.69
96.94
18.43
18.81
H Broader impacts
This paper proposes training-free, generalzzze,,,,, and robust image fusion method leveraging pre- trained latent diffusion model to generate high-quality fused images that serve both visual perception and machine decision-making Specifically, the study aims to advance the exploration and application of latent diffusion models in the field of image fusion, addressing two major challenges currently faced by image fusion tasks: generalization and robustness. is foreseeable that this work will have positive impact across various domains, particularly facilitating the advancement of information fusion technologies toward practical applications such as autonomous driving and intelligent medical diagnosis. The proposed approach poses minimal risks and is unlikely to have any significant negative effects.
28

---

Vicbie
Introred
DDEM
EMIAA
TC
COF
DOG
POA
Vis
9BJ36
Infeeee
DOFF
3/N NN99J336
EMAAK
IN 9BJ36
9BJ366
SN 9NJ3636
EN 9BJ36
9BJ36
Figure 11: Center zoomed-in views of qualitative infrared-visible visible image fusion results.
Figure 12: Center zoomed-in views of qualitative multi-exposure image fusion results.
Source
Source2
DeFlusion
ZNFF
The
De-MFIF
TC-MAA
PDFuse-B
The gnn
Source!
Sourde2
DellmEiF
CCF
PDFUse-8
Figure 13: Center zoomed-in views of qualitative multi-focus image fusion results.
29

---

Visible
DDFM
EMMA
TC-MaA
CCF
DCEvo
PDFuse-B
Infrared
DDBF
Text-IF
TIMFusion
Text-DiFuse
PDFuse-E
Figure 14: Qualitative comparison of PDFuse-Bi B, PDFuse-E, and state-of-the-ar methods on the TNO dataset.
TC-MoA
Infrared
MRFS
DDBF
Text-IF
TIMFusion
Text-DiFuse
PDFuse-E
Figure 15: Qualitative comparison of PDFuse-B, PDFuse-E, and state-of-the-art methods on the FMB dataset.
Visible
DDFM
EMMA
TC-MoA
CCF
DCEvo
PDFuse-B
Infrared
MRFS
DDBF
Text-IF
TIMFusion
Text-DiFuse
PDFuse-E
Figure 16: Qualitative comparison of PDFuse-B, PDFuse-E, and state-of-the-art methods the
MSRS dataset.
30

---

Visible
DDFM
EMMA
TC-MoA
CCF
DCEvo
PDFuse-B
Infrared
MRFS
DDBF
Text-IF
TIMFusion
Text-DiFuse
PDFuse-E
Figure 17: Qualitative comparison of PDFuse-B, PDFuse-F, and state-of-the-art methods on the RoadScene dataset.
Under
Over
SAMT.
HSDS.
TC-MoA
CCF
EAT
PDFuse-B
Figure 18: Qualitative comparison of PDFuse-B and state-of-the-art methods on the SICE dataset.
are
are
Source11
Source2
DeFusion
ZMFF
DB-MFIF
TC-MoA
CCF
PDFuse-B
Figure 19: Qualitative comparison of PDFuse-B and state-of-the-art methods on the Lytro dataset.
31

---

CT
MRI
DDFM
EMMA
TC-MoA
CCF
PDFuse-B
Figure 20: Qualitative comparison of PDFuse-B and state-of-the-art methods on the Havard medicine dataset.
32


Reviews:
[{"review_id": "PkYOGzyF95", "reviewer": "Reviewer_CeyL", "summary": "This paper proposes a training-free image fusion framework built on a pre-trained latent diffusion model. This framework presents two regularization mechanisms: Multi-Source Information Consistency Projection (MICP) and Latent Manifold-preservation Guidance (LMG). Then, the unified image fusion modeling is realized through the alternating regularization of the diffusion process by these two regularization mechanisms. Additionally, the method constructs a set of interference operators and integrates them into the two regularization mechanisms to tackle various degradations and interferences. They provide extensive experiments to verify this work’s effectiveness across multiple fusion scenarios and under complex interference conditions.", "questions": "- Is this method entirely training-free, or does it still require fine-tuning? Please clarify this point.\n- Why are the values set to 0 and $\\hat{\\bm{z}}_0^t$ respectively in Equation (13) during the alternating optimization setup?\n- The paper only considers two source images as input. Is the method adaptable to more input images, such as a sequence of three images with different exposure settings for multi-exposure image fusion?\n- Some minor issues include: Table 3 lacks ↑ or ↓ indicators for the metrics, and some experimental details, such as the diffusion step size, are missing.\n- Besides, please address the concerns in the Weakness part.", "limitations": "The authors discuss the limitations in the supplementary materials.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\n+ The idea of formulating the image fusion process as a latent diffusion process regularized by multi-source image conditions is novel.\n+ Designed two regularization mechanisms, MICP and LMG, to constrain the latent diffusion process, universally applicable to various image fusion tasks without requiring training. Meanwhile, a set of interference operators is constructed, which can be integrated into the regularized mechanisms to further enhance the overall robustness.\n+ The authors conduct extensive evaluations, proving the advanced performance of this work.\n+ The code is provided in the supplementary material, which is commendable.\nWeaknesses：\n- In the Multi-source Information Consistency Projection mechanism, the authors divide the optimization into two stages: the coarse structural component $\\mathcal{D}^s(\\hat{\\mathbf{z}}_0^t)$ and the fine detail component $\\mathcal{D}^d(\\hat{\\mathbf {z}}_0^t)$. However, it is puzzling that the subsequent equations optimize $\\mathcal{D}^s$ and $\\mathcal{D}^d$ without clearly defining their relationship.\n- After integrating the degradation operator $\\mathbf{\\Pi}$, the derivation from Equation (12) to Equation (16) is not entirely clear to me. Would it be possible to provide a more detailed explanation of this part?\n- When different source images encounter different degradation interferences, how to construct $\\mathbf{\\Pi}$ from $\\mathbf{\\Pi}_i$ and $\\mathbf{\\Pi}_j$. Although the authors provide a brief explanation in the supplementary materials, it would be helpful if more concrete details could be provided.\n- In the experimental section, the paper presents comparative results for both the base and enhanced versions of the method in infrared–visible image fusion, demonstrating promising performance; however, for multi-exposure and multi-focus image fusion, only the base version (PDFuse-B) results are reported.\n- The author conducted ablation studies on the MICP and LMG mechanisms, if further provided the regularization effects of these two mechanisms at each step of the latent diffusion process would offer a more intuitive demonstration.\n- Although the method achieves good results, its runtime is still relatively long (despite improvements in efficiency compared to similar diffusion-based methods).", "was_revised": true, "final_justification": "I would like to thank the author for his detailed response. He explained some unclear points in the formulas and symbols, and supplemented the efficiency experiments and some additional fusion comparison experiments. These experiments and analyses resolved my concerns. I think the article is of good quality and I will maintain my score.", "rebuttal": "**Q1**: The relationship between $\\mathcal{D}^s(\\hat{\\mathbf{z}}_0^t)$ and $\\mathcal{D}^s$, as well as between $\\mathcal{D}^d(\\hat{\\mathbf{z}}_0^t)$ and $\\mathcal{D}^d$.\n\n**Reply**: In fact, $\\mathcal{D}^s$ and $\\mathcal{D}^d$ are shorthand notations for the coarse structural component $\\mathcal{D}^s(\\hat{\\mathbf{z}}_0^t)$ and the fine detail component $\\mathcal{D}^d(\\hat{\\mathbf{z}}_0^t)$, respectively. We will include this clarification in the final version for notational consistency.\n\n\n**Q2**: Explanation of the process from Eq. (12) to Eq. (16) in the LGM mechanism.\n\n**Reply**: Eq. (12) represents the base version of the LGM mechanism, which regularizes the diffusion process by enforcing consistency between $\\hat{\\mathbf{z}}_0^t$ and multi-source images in both the latent space and the pixel space. \nWhen the interference operator set is introduced, the posterior sampling is further constrained by enforcing consistency between $\\mathbf{\\Pi} \\hat{\\mathbf{z}}_0^t$ and the multi-source images. This enables the model to preserve information fidelity while also handling degradation through $\\mathbf{\\Pi}$, as expressed in Eq. (16). Specifically, the left-hand side of Equation (16) replaces $\\hat{\\mathbf{z}}_0^t$ in the pixel-space loss with $\\mathbf{\\Pi} \\hat{\\mathbf{z}}_0^t$. The right-hand side constructs an optimization objective that combines a least-squares term and a regularization term: $$\\min_z \\left\\| \\mathbf{\\Pi} \\mathcal{D}(\\hat{\\mathbf{z}}_0^t) - \\max(i^m, i^n) \\right\\|_2^2 + \\left\\| \\mathcal{D}(\\hat{\\mathbf{z}}_0^t) \\right\\|_2^2,$$ which is solved via the normal equation.\n\n**Q3**: How to construct the unified operator $\\mathbf{\\Pi}$ from $\\mathbf{\\Pi}_i$ and $\\mathbf{\\Pi}_j$ when the multi-source image inputs exhibit different degradations.\n\n**Reply**: The construction of the unified operator $\\mathbf{\\Pi}$ from $\\mathbf{\\Pi}_i$ and $\\mathbf{\\Pi}_j$ depends on the fusion coefficients. When the source images undergo different types of degradation, the consistency loss between $\\mathbf{\\Pi} \\hat{\\mathbf{z}}_0^t$ and the multi-source images is established by treating $\\mathbf{\\Pi}$ as a fused operator derived from $\\mathbf{\\Pi}_i$ and $\\mathbf{\\Pi}_j$. The fusion coefficients are determined by the spatial weights of the multi-source images during each stage of regularization.\n\nTaking the first-stage projection of the MICP mechanism in the VIS task as an example, in order to preserve the saliency of the infrared image and the majority of scene details from the visible image, the fusion is typically performed using the $\\max(\\cdot)$ operator. In this case, the spatial fusion weights $\\text{coeff}_m$ and $\\text{coeff}_n$ satisfy the relation: $$ \\text{coeff}_m \\cdot i^m + \\text{coeff}_n \\cdot i^n = \\max(i^m, i^n).$$ Accordingly, the fusion of the interference operator sets $\\mathbf{\\Pi}_i$ and $\\mathbf{\\Pi}_j$ also follows this coefficient scheme. In the second stage of the MICP mechanism, the fusion coefficients for the interference operator sets are similarly derived based on the fusion weights computed over Sobel gradients. Each component of the LMG mechanism adheres to similar principles.\n\n**Q4**: For the multi‑exposure and multi‑focus image fusion tasks, only the base version, PDFuse‑B, was reported.\n\n**Reply**: PDFuse-B is the baseline version focusing solely on information fusion, while PDFuse-E is the enhanced version that integrates the interference operator set and can effectively handle various degradations in fusion scenarios. \n\nImage fusion can be divided into multi-modal image fusion and digital photography image fusion. In multi-modal image fusion, specific information metrics and loss settings are used to guide the fusion of different modality distributions, achieving the complementarity of multi-modal information. When degradations such as low light are present in multi-modal images, the information metrics in the fusion process will be affected, making it difficult to effectively aggregate the advantages of multi-modal images. Therefore, in the infrared-visible light image fusion task, we set up PDFuse-B and PDFuse-E to progressively evaluate the performance of the method, based on whether degradations are handled in multi-modal fusion. \n\nDigital photography image fusion aims to fuse multi-exposure and multi-focus images with different exposure and focus depth settings. Since exposure levels and focus depths in different source images are often complementary in different regions, PDFuse-B is enough to achieve complementary fusion of exposure levels or focus depth information in each region without the need for the enhanced version PDFuse-E with integrated degradation operators.  \n\n**Q5**: A more intuitive demonstration of the regularization effects of the MICP and LMG mechanisms.\n\n**Reply**: We collected a series of intermediate diffusion results under the regularization-constrained diffusion process, $\\mathcal{D}(\\hat{\\mathbf{z}}_0^t)$, which allows for an intuitive visualization of the effects imposed by different regularization mechanisms. From these results, it is evident that the MICP mechanism produces a pronounced enhancement of multi-source information, whereas the LMG mechanism yields an overall improvement in visual quality. Since the Rebuttal does not support posting figures, we will include this intuitive demonstration in the final version.\n\n**Q6**: Is fine‑tuning or any additional training required?\n\n**Reply**: No training or fine‑tuning is necessary. We define the multi‑source image fusion task as a regularized latent diffusion process conditioned on multi‑source images, which does not require any training or fine‑tuning of the LDMs. During inference, one needs only to inject the multi‑source images into the regularization mechanism, wherein the LDMs, guided by the regularization constraints, generate high‑quality fused images.\n\n**Q7**: The initial configuration of Equation (13) during alternating optimization.\n\n**Reply**: The MICP mechanism injects high‑fidelity information via a multi‑source image projection operator, whereas the LMG mechanism steers the distribution of intermediate latent variables through gradient guidance. Consequently, under an alternating optimization scheme, the initial settings for each mechanism differ. During MICP optimization, the LMG mechanism is disabled by setting the guiding gradient to zero. Conversely, during LMG optimization, the MICP mechanism is disabled by projecting onto the identity, i.e., setting the projection to $\\hat{\\mathbf{z}}_0^t$.\n\n**Q8**: Can the method support image fusion with more than two source images?\n\n**Reply**: Yes. Since our method formulates the fusion process as a regularized latent diffusion conditioned on multi‑source images, it does not impose an explicit limit on the number of inputs. Consequently, our approach can process an arbitrary number of source images in a single pass with negligible additional computational or temporal overhead. \n\nWe validated this capability on the SICE multi‑exposure dataset by inputting three images with different exposure levels. It is worth emphasizing that other methods can only process two images at a time, so they perform multi-exposure fusion of three images in two separate stages via sequential pairing. In contrast, our method achieves this in a single pass. The quantitative results are presented below:\n| Method| EI↑| SF↑| EN↑| AG↑| SD↑| TE↓| SCD↑| Q_cb↑|\n|-------|----|----|----|----|----|----|-----|------|\n|U2Fusion|85.165|22.561|7.225|8.048|54.565|13.255|0.716|**0.500**|\n|DeFusion|67.214|17.257|7.279|6.206|52.841|13.201|0.550|0.357|\n|HSDS-MEF|95.637|29.464|6.994|9.367|73.821|13.486|2.989|0.351|\n|TC-MoA  |89.575|24.851|7.532|8.611|57.462|12.947|0.959|0.466|\n|CCF     |86.120|28.723|7.614|8.885|**70.408**|12.866|**3.575**|0.461|\n|SAMT-MEF|89.485|29.514|7.425|9.378|58.071|13.055|1.225|0.430|\n|EAT     |36.849|9.384 |6.501|3.146|40.089|13.937|1.700|0.347|\n| **PDFuse**| **112.422** | **30.975** | **7.708** |**10.930** |68.581(#2) |**12.771** |2.349(#3)|0.489(#2)|\n\nFrom the experimental results, our approach achieves leading performance on most evaluation metrics (#1 or #2), demonstrating that our regularization mechanism can be generally applied to fuse three or more source images. In addition, we further analyzed how the runtime and the Edge Intensity (EI) of the fused images produced by our method, PDFuse, change as the number of input images with different exposure settings varies. The quantitative results are presented in the table below:\n| Input sequence count |2|3|4|5|6|7|8|9|\n|-|-|-|-|-|-|-|-|-|\n| EI↑|95.155 |112.422|117.805|119.518|121.840|121.855|123.009|**124.45**|\n| Runtime (s)|18.08|18.15|18.03|18.24|18.33|18.19|18.30|18.23|\n\nIt can be observed that, for the same scene, as the number of input images with different exposure settings increases, the scene information texture (EI) of the fused images gradually improves, while the runtime remains essentially unchanged.\n\n**Q9**: Time efficiency\n\n**Reply**: Compared with existing diffusion-based methods such as DDFM and CCF, our method achieves shorter sampling times while delivering superior performance. In contrast to other end-to-end fusion methods requiring multi-stage serial processing to handle more than two source images, our method theoretically requires only a single run to process an arbitrary number of source images. Therefore, these end-to-end methods will lead to a growth by multiples in runtime as the number of source image sequences grows, whereas our method maintains nearly constant runtime. \n\n**Q10**: Lack of indicator ↑, ↓ and diffusion compensation settings.\n\n**Reply**: Thanks for pointing out these issues. We will carefully address all the omissions and shortcomings in the camera ready and further improve the presentation of the figures and tables."}, {"review_id": "ZqS0lSRAH6", "reviewer": "Reviewer_8Yc3", "summary": "This paper proposes **PDFuse**, a training-free, general-purpose, and degradation-robust image fusion framework based on pretrained latent diffusion models. The key idea is to reformulate image fusion as a posterior sampling process guided by two regularization mechanisms:\n- **MICP (Multi-source Information Consistency Projection)**: injects multi-source image content via a two-stage projection strategy.\n- **LMG (Latent Manifold-preservation Guidance)**: enforces generative consistency by aligning latent variables with the manifold prior.\n\nTo further enhance robustness, a set of degradation operators is embedded into both mechanisms, allowing PDFuse to adapt to various degradation scenarios. Extensive experiments across diverse fusion tasks (IVF, MEF, MFF) and semantic segmentation verify the superiority and robustness of the proposed approach.", "questions": "- How does the method scale to fusion tasks involving more than two source images?\n- Can the proposed degradation operators generalize to unseen types or compositions?", "limitations": "See above.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### **Strengths**\n\n1. **Well-engineered and effective framework**  \n   The alternating design of MICP and LMG offers a principled mechanism to balance fidelity and generative quality, and integrates seamlessly into latent diffusion inference.\n\n2. **Training-free and broadly applicable**  \n   The method does not require retraining and generalizes across tasks and degradations, making it practically useful. The integration of an interference operator set further improves robustness.\n\n3. **Comprehensive experiments**  \n   Strong empirical results on multiple tasks, supported by ablation studies and semantic-level verification, demonstrate the method's effectiveness and generalization.\n\n---\n\n### **Weaknesses**\n\n1. **Limited theoretical novelty**  \n   The approach is largely driven by system design and heuristics. There is no formal analysis of the convergence, optimality, or theoretical behavior of the alternating regularization scheme.\n\n2. **Somewhat heuristic projection and loss design**  \n   The two-stage MICP and the handcrafted LMG loss rely on pre-defined forms (e.g., max intensity, Laplacian regularization), which may limit adaptability beyond the chosen tasks.\n\n3. **Lack of analysis on degradation generalization**  \n   The degradation operator set is conceptually flexible, but it’s unclear how well it handles unseen or compound degradations at test time.", "was_revised": true, "final_justification": "The response has addressed my concerns, for which I lean towards acceptance of this paper.", "rebuttal": "**Q1**: Analysis of the convergence, optimality of the alternating regularization scheme.\n\n**Reply**: Our method defines image fusion as a latent diffusion process with multi-source regularization constraints, which is the first use of pre-trained LDMs for a training-free, general, and robust fusion approach.\n\n**Convergence and Optimality Analysis:**\n\n(1) The MICP mechanism regularizes the diffusion process by projecting the latent intermediate variable $\\hat{\\mathbf{z}}_0^t$ in two stages: first, the projection of the coarse structural component to obtain $\\mathcal{D}^s(\\hat{\\mathbf{z}}_0^t)$, followed by the projection of the fine detail component $\\mathcal{D}^d(\\hat{\\mathbf{z}}_0^t)$. This two-stage approach mitigates gradient conflicts that arise from optimizing multiple objectives simultaneously. Notably, both projection stages can be solved using conjugate gradient iterative modeling. In Appendix A.1, we analyze the coefficient matrices for the conjugate gradient solution of the two-stage projection. It has been proven to be symmetric and positive definite, ensuring strict convexity. Therefore, the MICP mechanism is convergent, and the optimal solution is guaranteed within a finite number of iterations.\n\n(2) The LMG mechanism enforces consistency between $\\hat{\\mathbf{z}}_0^t$ and multiple source images through pixel-level and latent space-level regularization. The pixel-level regularization term is derived from widely used loss functions in the image fusion field **(MURF，TPAMI 2023；SHIP，TPAMI 2024)**. However, the many-to-one mapping in VAE decoding can lead to multiple optimization directions when relying solely on pixel-level gradients. Latent space-level regularization resolves this issue, guiding $\\hat{\\mathbf{z}}_0^t$ toward a more definitive optimization direction, thus improving convergence. Despite this, because the loss function involves multi-objective optimization and is non-convex, it often leads to convergence at local optima.  \n\n(3) Given that image fusion tasks lack ground truth (GT), multiple quantitative metrics and human visual assessment are used for performance evaluation. Additionally, given the complexity of posterior sampling in the diffusion process, it is challenging to apply specific theoretical tools to guarantee optimal fusion performance of alternating optimization. However, we have validated the effectiveness of the alternating optimization regularization mechanisms as thoroughly as possible through ablation experiments. In the ablation study presented in the main text, we evaluated the first projection stage of the MICP mechanism (Method II), the second stage (Method III), and the LMG mechanism (Method I) from both qualitative and quantitative perspectives. The results clearly show that the method with alternating optimization outperforms any individual regularization mechanism, fully validating its effectiveness.\n\n\n\n**Q2**: The adaptability of the projection and loss design.\n\n**Reply**: The two fundamental elements of an image are pixel intensity and gradient, representing contrast distribution and texture structure, respectively. In the multi-source image fusion, we aim to maintain significant contrast while preserving the richest texture details. This concept is applicable to most image fusion tasks. In our projection setup, the overall contrast distribution is projected based on the pixel intensity of the fusion scene, adapting to various fusion scenarios. For instance, in infrared-visible image fusion, we aim to capture the maximum intensity to ensure the saliency of thermal objects while preserving the texture of visible modality to the greatest extent. For multi-exposure image fusion, our focus is to ensure the fused image's overall contrast aligns with the desirable exposure level while maximizing the integration of scene structures. Our adopted loss based on pixel intensity and gradient is widely recognized and used in the image fusion field (MURF, TPAMI 2023; SHIP, TPAMI 2024). \n\nTo evaluate the broader applicability of the proposed method across various image fusion tasks, we also test its performance in multi-polarization image fusion, which integrates imaging data from different polarization angles to fully capture the scene's polarization. The quantitative results are as follows:\n\n|Method|EI↑|SF↑|EN↑|AG↑|SD↑|TE↓|SCD↑|Q_cb↑|\n|-|-|-|-|-|-|-|-|-|\n|U2Fusion|24.573|8.279|5.143|2.342|14.887|6.886|1.280|0.418|\n|DeFusion|28.454|8.178|6.448|2.616|31.290|5.580|1.543|0.353|\n|DDFM|37.094|12.696|6.518|3.649|30.066|5.513|1.884|0.384|\n|EMMA|39.968|11.703|6.753 |3.704|38.674|5.276|1.699|0.387|\n|TC-MoA|43.186|14.358|6.646|4.187|34.115|5.382|1.768|0.387|\n|CCF|45.828|17.102|6.382|4.931|25.846|5.646|1.671|0.382|\n|DCEvo|43.249|14.682|6.703|4.193|36.551|5.326|1.771|0.413|\n|PDFuse|**48.941**|**15.687**|**6.796**|**4.681**|37.523(#2)|**5.232**|1.808(#2)|0.389(#3)|\n\nIn multi-polarization image fusion, our method still excels in texture details, contrast, correlation, and structural consistency, showcasing strong generalization performance.\n\n**Q3**: Extending to fusion tasks involving more than two source images\n\n**Reply**: Since our method does not impose an explicit limit on the number of inputs. Consequently, our approach can process an arbitrary number of source images in a single pass with negligible additional computational or temporal overhead. When handling more than two source images, the MICP mechanism concurrently analyzes the pixel- and texture-level details of all inputs. It injects them into the diffusion process via its two-stage projection. Similarly, the LMG mechanism establishes gradient‑based constraints across all source images. The underlying principles and workflow of the mechanisms remain unchanged; it only requires increasing the number of information sources within the mechanisms.\nWe validated this capability using the SICE multi-exposure dataset by inputting three images with varying exposure levels. It is worth emphasizing that other methods can only process two images at a time, so they perform multi-exposure fusion of three images in two separate stages via sequential pairing. In contrast, our method achieves this in a single pass. The quantitative results are presented below:\n\n|Method|EI↑|SF↑|EN↑|AG↑|SD↑|TE↓|SCD↑|Q_cb↑|\n|-|-|-|-|-|-|-|-|-|\n|U2Fusion|85.165|22.561|7.225|8.048|54.56|13.25|0.716|**0.500**|\n|DeFusion|67.214|17.257|7.279|6.206|52.841|13.201|0.550|0.357|\n|HSDS-MEF|95.637|29.464|6.994|9.367|73.821|13.486|2.989|0.351|\n|TC-MoA|89.575|24.851|7.532|8.611|57.462|12.947|0.959|0.466|\n|CCF|86.120|28.723|7.614|8.885|**70.408**|12.866|**3.575**|0.461|\n|SAMT-MEF|89.485|29.514|7.425|9.378|58.071|13.055|1.225|0.430|\n|EAT|36.849|9.384|6.501|3.146|40.089|13.937|1.700|0.347|\n|**PDFuse**  |**112.422**|**30.975**|**7.708**|**10.930**|68.581(#2)|**12.771**|2.349(#3)|0.489(#2)|\n\nFrom the experimental results, although our method requires only a single regularized diffusion step to process three multi-exposure images—whereas other methods must perform multiple sequential fusion stages—our approach still achieves leading performance on most evaluation metrics (#1 or #2), demonstrating that our regularization mechanism can be generally applied to fuse three or more source images. In addition, we further analyzed how the runtime and the Edge Intensity (EI) of the fused images produced by our method, PDFuse, change as the number of input images with different exposure settings varies. The quantitative results are presented in the table below:\n\n| Input sequence count |2|3|4|5|6|7|8|9|\n|-|-|-|-|-|-|-|-|-|\n| EI↑|95.156|112.42|117.81|119.52|121.84|121.86|123.01|**124.45**|\n| Runtime (s)|18.08|18.15|18.03|18.24|18.33|18.19|18.30|18.23|\n\nQuantitative analysis shows that as the number of input images with varying exposure settings increases, the scene texture (EI) of the fused images improves, while the runtime remains largely unaffected.\n\n**Q4**: Handle compound and invisible degradations\n\n**Reply**: The flexible interference operator set we designed accommodates various degradations in multimodal image fusion. In the actual design,  we have aimed to incorporate as many degradation types as possible, such as low light, exposure, blur, and thermal diffusion. Notably, our method is not limited to handling single degradation types but can also address composite degradations. For example, in the main text, we demonstrate the challenging scenario of low light and blur composite degradation in Fig. 3, where our method effectively mitigates the degradation, showcasing its superior fusion capabilities. To further validate its generalization, we evaluated our method on several real degradation datasets, in Appendix B (Fig.s1, Fig.s2, Fig.s3, Fig.4), which include both well-defined degradations, such as low light, improper exposure, and blur, as well as more complex, unseen degradations like low contrast, fog, glare, and compression losses that were not explicitly defined in the interference operator set. We analyze that the handling of composite degradations can be achieved by combining and integrating single degradations into the regularization mechanism. Although real, unseen degradations may not be precisely modeled, they often contain already defined degradation types or share similarities with defined degradations, such as low contrast, potentially containing some improper exposure. \n\nIn general, the flexible compatibility of our method with the interference operator set enables it to effectively handle composite degradations, including previously unseen types. Furthermore, we would like to emphasize that our method is the first to utilize a pre-trained latent diffusion model (LDM) for unsupervised degradation handling in image fusion. By leveraging the LDM, it can recover texture details lost due to degradation, achieving satisfactory performance. This approach offers new insights for research on unsupervised robust fusion methods."}, {"review_id": "AXZ5tTOIon", "reviewer": "Reviewer_iFAK", "summary": "This paper introduces PDFuse, a training-free image fusion framework built on pre-trained latent diffusion models with a novel projection–manifold regularization strategy. \n\nBy formulating fusion as a constrained diffusion inference process, it effectively integrates multiple source images to generate high-fidelity results. \n\nTwo core mechanisms, Multi-source Information Consistency Projection (MICP) and Latent Manifold-preservation Guidance (LMG), ensure both consistency with source inputs and alignment with the generative prior. \n\nExtensive experiments show that PDFuse delivers competitive performance across diverse image fusion tasks without requiring supervised training.", "questions": "### Questions for the Authors\n\n**Q1:** Lines 61–62 state that \"PDFuse constructs a canonical interference operator set.\"  \n- What exactly is meant by a *canonical interference operator set* in this context?  \n- Why is this set necessary, as mentioned again in Line 173?  \n- Please clarify its role in the method and consider adding an ablation or analysis to demonstrate its impact.\n\n**Q2:** The paper heavily emphasizes fusion from multiple source images (e.g., `i_m`, `i_n`), but **Figure 1** and **Figure 2** do not visually explain how these sources are processed or interact during inference.  \n- Can the authors revise or augment the diagrams to clearly depict the role and flow of multiple inputs?\n\n**Q3:** The qualitative results in **Figures 3, 5, and 6** do not convincingly show improvements of the proposed method.  \n- Could the authors enhance the visualization quality and include zoomed-in or region-focused comparisons to better highlight differences?  \n- Also, **Figure 5** is not referenced or discussed in the text—please explain its purpose and relevance.\n\n**Q4:** The paper includes many tables and figures, but lacks clear conceptual takeaways or technical insights.  \n- Can the authors restructure the results section to highlight key observations and explain what we learn from each experiment, rather than simply reporting numbers?\n\n**Q5:** The ablation study is quite shallow and lacks detailed analysis.  \n- Can the authors expand this section to better isolate the contributions of individual components (e.g., MICP, LMG, interference operators) with clear comparisons?\n\n**Q6:** In **Table 2**, the comparison with **EAT (TMM’25)** is inconclusive due to missing metrics.  \n- Can the authors either reproduce the missing metrics for a fair comparison or discuss this limitation more clearly to help contextualize their method’s relative performance?", "limitations": "Yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n- The submission is complete, and the writing is generally clear and accessible to readers.  \n- The method is training-free, which reduces computational cost and simplifies deployment.  \n- Some quantitative results demonstrate the potential of the proposed approach.\n\n**Weaknesses**\n- The paper emphasizes the role of multiple source images (e.g., `i_m`, `i_n`), yet the visual diagrams (e.g., **Figure 1** or **Figure 2**) do not illustrate how these sources interact with the fusion process, making the method less intuitive to understand.  \n- The qualitative results are not convincing, **Figures 3, 5, and 6** do not clearly show visual improvements from the proposed method, and **Figure 5** appears unexplained or unreferenced in the main text.  \n- The paper feels overloaded with tables and figures, but lacks clear insights or takeaways. The **ablation study** section is shallow and does not offera  meaningful understanding of the proposed components or their contributions.  \n- In **Table 2**, comparisons to baselines like **EAT (TMM’25)** are incomplete due to missing metrics, making it difficult to assess the true advantage of the proposed method.", "was_revised": true, "final_justification": "After reviewing it, I find that it partially addresses my concerns. The proposed method presents some meaningful contributions, particularly the integration of the interference operator set and the detailed ablation studies. However, the overall clarity and organization of the paper remain challenging to follow. Given these considerations, I slightly raise my score to reflect the improvements. Additionally, some of the metrics introduced by the authors are unfamiliar to me, so I have lowered my confidence and will defer to other reviewers and the ACs for the final judgment.", "rebuttal": "**Q1**: Analysis of the canonical interference operator set.\n\n**Reply**: Multi-modal image fusion is often limited by scene degradations, hindering the effective integration of advantages from different modalities. To improve the robustness of fusion methods against such degradations, we constructed **a canonical interference operator set**, consisting of a series of explicit models for common degradations such as low light, improper exposure, blur, and thermal diffusion. \n\nInspired by PSLD (NeurIPS 2023), we incorporated this operator set into the regularization processes of the MICP and LMG mechanisms, utilizing diffusion priors to address these degradations. Specifically, the two-stage projection of MICP, as defined in Eq. (8) and Eq. (10), is modified by the interference operator set, as shown in Eq. (14) and Eq. (15). Similarly, the LMG mechanism, as in Eq. (12), is adjusted after integrating the operator set, as shown in Eq. (16). This integration allows the diffusion process to address the inverse problem of the degradation operator $\\mathbf{\\Pi}$, effectively mitigating various degradations in multi-modal image fusion. To validate the effectiveness of the canonical interference operator set, we evaluated its de-degradation capability in challenging scenarios, as illustrated in Fig. 4 and Table. 1. In the ablation experiments, we compared PDFuse-B (without the interference operator set) and PDFuse-E (with the interference operator set) in Fig. 8 and Table 5. The experimental results demonstrate that PDFuse-E, which integrates the interference operator set, more effectively handles degradations such as low light and blur, outperforming PDFuse-B in the multi-modal fusion process.\n\n**Q2**: Enhance the intuitiveness of the information interaction process across multiple sources.\n\n**Reply**: We sincerely appreciate the improvement suggestions you have provided. Specifically, we have designed two efficient multi-source image regularization mechanisms to constrain the diffusion process. These mechanisms first extract key information, such as coarse structure, pixel intensity, and texture, from the multi-source images through information metrics. Then, they inject this information into the diffusion process through projection and gradient constraints. \nThe MICP mechanism ensures the information fidelity of the fused image through two-stage projection regularization. On the other hand, the LMG mechanism simultaneously constructs gradients between $\\hat{\\mathbf{z}}_0^t$ and the multi-modal images in both pixel and latent spaces, guiding the diffusion process to generate high-quality fusion results. \nIn the camera ready, we commit to incorporating intuitive representations of these interactions and expand the framework diagram as in Fig. 2 to more clearly illustrate the multi-modal information interaction process.\n\n**Q3**: The visualization of the qualitative results in Fig. 3, Fig. 5, and Fig. 6, ​​with an explanatory note on Fig. 5​.\n\n**Reply**: Fig. 5 presents the qualitative results for multi-exposure image fusion. We discussed these results in line 218 of the main text, but we are sorry for mistakenly referring to Fig. 4. We provide several explanations to help convey the visual advantages of our method.\n\n(1) Fig. 3 presents the qualitative results for infrared-visible image fusion. According to the qualitative analysis, our method exhibits superior infrared salience in pedestrians and effectively preserves fine details in both infrared and visible textures (e.g., bicycle and tree trunk textures).\n\n(2) Fig. 5 presents the qualitative results for multi-exposure image fusion. Our method shows a significant advantage in overall exposure levels and color contrast compared to other methods. Additionally, the highlighted regions confirm the superiority of our method in preserving textures in different exposure images (e.g., windows, posters, clouds). \n\n(3) Fig. 6 presents the qualitative results for multi-focus image fusion. Our method is capable of preserving textures across different focus depths while maintaining a high overall image contrast. Specifically, compared to the similar diffusion method CCF, our method shows a significant improvement in texture fidelity.  \nThanks for pointing out this issue. We will carefully correct all typos in the final version and further include zoomed-in or region-focused comparisons to enhance the presentation of the figures and tables.\n\n**Q4**: Reorganize the results section and provide specific analysis.\n\n**Reply**: **Reply**: We commit to reorganizing the results section to present a deeper theoretical analysis in the camera ready. Here, we attempt to analyze the experiments in conjunction with the underlying principles of our method to provide valuable insights.\n\n(1) We evaluated our method on VIF, MEF, and MFF tasks. Compared to the diffusion-based CCF of the same class, our approach demonstrates superior texture preservation, particularly in MFF in Fig. 6 and Table. 3. This stems from key differences in regularization. CCF guides the DDPM process using pixel-level gradients, where large gradients risk over-shifted results, necessitating small, multi-step updates for fidelity. In contrast, our method employs the MICP mechanism with a multi-stage dual projection strategy, enabling hierarchical, convergent optimization of coarse structures $\\mathcal{D}^s(\\hat{\\mathbf{z}}_0^t)$ and fine details $\\mathcal{D}^d(\\hat{\\mathbf{z}}_0^t)$ while mitigating conflicts. Consequently, our method achieves more effective texture retention than CCF.\n\n(2) Furthermore, as shown in the qualitative results (Figs. 3, 5, and 6), our method achieves superior contrast and color fidelity across tasks compared to other fusion approaches, and in some cases even surpasses the multi-source images. This is further supported by the higher SD values reported in Tables. 1, 2, and 3. These enhancements result from the combined effect of the MICP and LGM mechanisms. MICP enables efficient and high-fidelity preservation of multi-source information, and LGM guides the latent variable $\\hat{\\mathbf{z}}_0^t$ to maintain diffusion consistency with the source images through gradient-based regulation. These allow the generative model to enhance overall image quality in terms of contrast and color, while simultaneously injecting source image information, thereby achieving superior performance in these aspects.\n\n**Q5**: A more detailed analysis of the ablation experiments.\n\n**Reply**: In the ablation study, we evaluated the removal of the LMG mechanism (Method I), the first stage of MICP (Method II), the second stage of MICP (Method III), the complete MICP mechanism (Method IV), as well as variants without (PDFuse-B) and with (PDFuse-E) the interference operator set.\n\nThe results reveal that removing the first projection stage of the MICP mechanism leads to color distortion, as this projection  stage is responsible for injecting the coarse structural component $\\mathcal{D}^s(\\hat{\\mathbf{z}}_0^t)$ into the diffusion process. Without it, low-frequency and color information cannot be effectively preserved. Removing the second projection stage results in significant texture loss, since it handles the injection of fine detail components $\\mathcal{D}^d(\\hat{\\mathbf{z}}_0^t)$ into the diffusion process. When the entire MICP mechanism is removed, relying solely on the LMG mechanism fails to maintain information fidelity, leading to poor fusion quality. Conversely, when the LMG mechanism is removed, the MICP mechanism still supports structural and texture preservation. However, compared to PDFuse-B (which includes the LMG mechanism), the fusion results show reduced contrast and darker tones due to the absence of manifold-preservation guidance from the LMG mechanism, resulting in limited use of generative priors. Finally, PDFuse-E, which integrates the interference operator set, outperforms PDFuse-B by leveraging diffusion priors to solve inverse degradation problems. It effectively handles low light, blur, and other distortions, yielding better overall visual quality.\n\nWe commit to including these detailed ablation analyses of the MICP mechanism, LMG mechanism, and the interference operator set in the final version to better emphasize the contributions of each component.\n\n**Q6**: Additional metrics are introduced and compared with EAT.\n\n**Reply**: Due to the lack of ground truth (GT) in image fusion tasks, existing methods largely adopt unsupervised metrics and some reference-based metrics for performance evaluation. Our method employs evaluation metrics including SF, EI, AG, SD, SCD, and Q_cb, which are widely used in mainstream approaches (such as the selected competitors: CCF, TC‑MoA, and HSDS‑MEF).  Notably, the metrics used in EAT are not the standard ones commonly adopted in multi‑exposure image fusion (MEF). To enhance the comparison comprehensiveness, we additionally introduce **MEF‑SSIM** (IEEE TIP 2015) for evaluation. MEF‑SSIM is a structural similarity evaluation metric specifically designed for MEF, which has been widely adopted and recognized in the MEF field. This metric performs perception‑driven quality assessment by measuring the consistency between the fusion result and the ideal structures of multi‑source exposure images. We follow the setup of the main text and conduct tests on the MEFB dataset, with the implementation code obtained from MEFNet (IEEE TIP 2019). The quantitative experimental results are shown below:\n\n| Method|U2Fusion|DeFusion|HSDS‑MEF|TC‑MoA |CCF|SAMT|EAT|**PDFuse (Ours)**|\n|-|-|-|-|-|-|-|-|-|\n|**MEF‑SSIM**|0.8030|0.8297|0.8164|0.8838|0.7963|0.8617|0.8574|**0.8856**|\n\nAs shown, our method still achieves the highest performance under the MEF‑SSIM metric, further validating its fusion quality effectiveness and advancement."}, {"review_id": "y7Q9sEhNen", "reviewer": "Reviewer_2sqp", "summary": "This paper proposes a training-free and unified framework, named PDFusion, which incorporates the diffusion posterior sampling process into a pre-trained latent diffusion model. It introduces MICP and LMG to guide the generation process, aiming to improve fidelity and quality. Additionally, the framework claims the ability to handle degraded inputs inherently. Extensive experiments across various fusion scenarios and downstream tasks demonstrate the method's robustness.", "questions": "- Empirically, the pre-trained VAE is unable to generate high-quality visible and infrared fused images, as it has never been exposed to such data during training. It remains unclear how the model is guided in the latent space and how it decodes these representations to synthesize high-fidelity fusion images. Additionally, Appendix A.3 is difficult to follow: the algorithm references Equations (10)–(15), but these equations are either missing or poorly integrated, resulting in a presentation that appears chaotic and incomprehensible.\n- The performance of PDFuse-E appears questionable, both in terms of underlying principles and the reported metric values.", "limitations": "Yes", "rating": 2, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\n- The motivation is novel. PDFusion leverages a pre-trained latent diffusion model to guide the fusion process in the latent space.\n- The use of a canonical interference operator set allows PDFusion to handle various types of degradation and produce higher-quality fusion results.\n\n### Weaknesses\n\n- PDFusion is based on Stable Diffusion but is guided at the pixel level. In other words, the fusion occurs at the pixel level and is subsequently encoded into the latent space. This approach undermines the advantage of latent-space fusion, making it fundamentally similar to conventional DDPM approaches.\n- Table 1 reports an unusually large performance gain for PDFuse-E in metrics such as EI, SF, and AG. It is difficult to understand how degraded inputs, which have lost information, can produce outputs of higher quality than those generated from clean inputs. This discrepancy raises concerns regarding the credibility and reliability of the reported results.\n- While the method is described as training-free, it remains unclear whether the de-degraded outputs are guided using clean inputs. If so, this undermines the validity of the \"training-free\" claim. If not, it is unclear how the method ensures clean fusion outputs under pixel-level constraints with degraded inputs.\n- The paper does not clearly explain how the datasets $\\mathcal{D}^s$ and $\\mathcal{D}^d$ are constructed or obtained.\n- Every generation step includes a decoding operation. Given this, it is difficult to understand how PDFusion achieves faster runtime than DDFM and CCF, as reported in Table S6.\n\n>  If the authors can clarify my concerns, I would be inclined to significantly raise my score.", "was_revised": true, "final_justification": "- First, my main question regarding the rationale behind the unusually large performance gains of PDFuse-E, which reportedly doubles or even triples the performance of prior SOTA methods, remains unanswered. Such substantial improvements appear revolutionary, yet they are achieved solely with a training-free model. This raises serious concerns about the fairness and reliability of the metrics presented in the paper and significantly affects my confidence in the reported results.\n\n- Second, although LDMs are generally faster than DDPMs due to latent-space denoising, the proposed method employs a decoder at every denoising step to reconstruct latent images—an architectural choice that likely negates the typical speed advantage of LDMs. Moreover, the comparison is made only against current DDPM-based approaches, which is an unfair baseline and risks overstating the claimed efficiency gains. In addition, the authors’ assertion that “our runtime remains nearly constant as the number of input images increases, while that of others grows roughly linearly” is unconvincing and counterintuitive, further undermining my confidence in the authenticity of the reported results.\n\n- Third, the degradation operator is applied to the decoded $x$; however, this approach is not grounded in the core principles of LDMs. \n\nGiven that these core issues remain unresolved, I will retain my original score.", "rebuttal": "**Q1**: Explanation of $\\mathcal{D}^s$ and $\\mathcal{D}^d$.\n\n**Reply**: $\\mathcal{D}^s$ and $\\mathcal{D}^d$ are the two projection stages of the MICP regularization constraint, not additional datasets. Specifically, to mitigate the gradient conflict caused by simultaneously optimizing intensity loss and gradient loss directly on the fused image and to enhance the efficiency of the regularization, we divided the regularized projection of the MICP mechanism into two stages. In the first stage, we focused on optimizing intensity loss to obtain the coarse structural component $\\mathcal{D}^s(\\hat{\\mathbf{z}}_0^t)$ as in Eq. (8), abbreviated as $\\mathcal{D}^s$. In the second stage, we treated texture as an additional layer optimization to ensure the most detailed texture while maintaining the overall intensity structure unchanged, generating a fine detail component $\\mathcal{D}^d(\\hat{\\mathbf{z}}_0^t)$, abbreviated as $\\mathcal{D}^d$ as in Eq. (10). The two projection components were combined to produce the final projection result.\n\n**Q2**: Explanation on VAE Fidelity.\n\n**Reply**:  Actually, VAE does exhibit significant information bias and loss when applied to high-quality visible images with very dense and regular textures. However, the loss introduced by VAE is typically concentrated in its high-frequency components (TVT, ICCV 2025). We performe a 2D FFT on both the original image and the VAE-encoded and decoded reconstructed image, generating a radial frequency grid followed by frequency band partitioning, which is used to study the distribution of MSE losses from VAE encoding and decoding across different normalized frequency bands on the LLVIP dataset, as shown below:\n\n\n|Frequency Range (cycles/pixel)|0.00-0.025|0.025-0.050|0.050-0.100|0.100-0.250|0.250-0.500|\n|-|-|-|-|-|-|\n| MSE|0.000000|0.000001|0.000003|0.000062|0.001003|\n\nIt is clear that VAE's loss and reconstruction bias are concentrated in the high frequencies. To address this challenge, we adopted a high-frequency residual injection strategy. Specifically, in our MICP mechanism, the regularized projection captures the coarse structural component $\\mathcal{D}^s(\\hat{\\mathbf{z}}_0^t)$ and the fine detail component $\\mathcal{D}^d(\\hat{\\mathbf{z}}_0^t)$ from the multi-source image set {$i^m, i^n$}. Then, a high-frequency filter $\\mathcal{G}(\\cdot)$ is applied to remove the high-frequency components from the projection result as in Eq. (11). In other words, the high-frequency information is kept as $R$ rather than encoded into the latent space. Moreover, $R$ is iteratively refined via projection regularization of $\\hat{\\mathbf{z}}_0^{t-1}$ and finally injected as a residual after the process completes. (see Appendix A.3).\n\nAdditionally, we further conduct an ablation study on the high-frequency filtering and residual structure. The MICP mechanism projection result is directly encoded by VAE into the latent space (without pre-reserving high-frequency information), and the quantitative comparison is shown in the table below:\n\n|Method|EI↑|SF↑|EN↑|AG↑|SD↑|TE↓|SCD↑|Q_cb↑|\n|-|-|-|-|-|-|-|-|-|\n|wo/(filtering & residual) |39.391|8.070|7.359|3.371|45.077|7.528|0.913|0.418|\n|PDFuse-B|48.519|11.028|7.427|4.221|47.931|7.457|0.981|0.437|\n\nBased on the ablation results, the model variant lacking high-frequency filtering and residual components exhibits a significant loss in edge and texture details. These results demonstrate the effectiveness of our adopted high-frequency residual injection strategy.\n\n**Q3**: Explanation of the algorithm process in Appendix A.3.\n\n**Reply**: The equations referenced in the algorithm in Appendix A.3 correspond to those presented in the main text, rather than any missing ones. We apologize for the misunderstanding. We added the symbol $†$ to distinguish these equations from those in the appendix and provided an explanation after the algorithm. \n\n**Algorithm process:** After each step of diffusion, the intermediate variable $\\hat{\\mathbf{z}}_0^t$ is obtained and then constrained through the alternating optimization regularization mechanism. If the MICP mechanism is adopted, the first-stage projection is performed using Eq. (8) in the main text to obtain $\\mathcal{D}^s(\\hat{\\mathbf{z}}_0^t)$, followed by the second-stage projection using Eq. (10) to obtain $\\mathcal{D}^d(\\hat{\\mathbf{z}}_0^t)$. After updating the high-frequency residual $R$, the projection result is weighted and encoded into the latent space using Eq. (11), with the gradient of LMG mechanism set to 0. If the LMG mechanism is adopted, Eq. (12) is used to establish the consistency gradient between pixel space and latent space to guide $\\hat{\\mathbf{z}}_0^t$, while the MICP mechanism performs the identity projection of $\\hat{\\mathbf{z}}_0^t$. The update of $\\mathbf{z}_t$ is completed by applying the regularization constraint with Eq. (6). The PDFuse-E process follows the same steps as PDFuse-B, with the distinction that its MICP and LMG mechanisms in Eq. (14) ~ Eq. (16) integrate the interference operator set $\\mathbf{\\Pi}$. \n\nIn the camera ready, we will merge the main text and the appendix, unify the numbering of the equations, and include more detailed explanations of the algorithm process to enhance the clarity of the algorithm description.\n\n**Q4**: Time Efficiency Analysis of PDFuse, CCF, and DDFM.\n\n**Reply**: In all experiments, we adhered to the default settings from the DDFM and CCF papers, with diffusion steps set to 100 and 200, respectively, while our method, PDFuse, was configured with 50 steps. Despite using fewer diffusion steps than DDFM and CCF, PDFuse outperforms both methods across all tasks. Furthermore, we compared PDFuse, CCF, and DDFM with a uniform diffusion step count of 100, and the resulting running time comparisons are shown in the table below:\n\n|Method|Runtime (100 steps) / Second|\n|-|-|\n|DDFM|55.29|\n|CCF|50.51|\n|PDFuse|**32.63**|\n\nOur method is still faster than DDFM and CCF, which is due to the higher efficiency of latent space diffusion compared to pixel space diffusion.\n\n**Q5**: Explanation of the principles and performance of PDFuse-E.\n\n**Reply**:  First, we would like to clarify that our approach does not require any training, which achieves image fusion through an alternating regularized existing latent diffusion process. Secondly, our method does not rely on any clean input to guide the degradation removal. Instead, it addresses the degradation by integrating the interference operator set. Next, we will provide a detailed clarification and illustration of this approach.      \n\nActually, when multi-source images $\\{i^m, i^n\\}$ suffer from degradations (such as Gaussian blur), the loss (such as the maximum intensity loss $min_{\\hat{\\mathbf{z}}_0^t}\\left\\| \\mathcal{D}(\\hat{\\mathbf{z}}_0^t) - \\max(i^m, i^n) \\right\\|_2^2$) will also cause the final result $\\mathcal{D}({\\mathbf{z}}_0)$ to also exhibit degraded. In this context, our PDFuse-E integrates the interference operator set $\\mathbf{\\Pi}$ into the regularization constraints (such as explicitly modeling the Gaussian blur operator $\\mathbf{\\Pi}_g$), and the maximum intensity loss is transformed into:\n\n$min_{\\hat{\\mathbf{z}}_0^t}\\left\\| \\mathbf{\\Pi}_g \\mathcal{D}(\\hat{\\mathbf{z}}_0^t) - \\max(i^m, i^n) \\right\\|_2^2$. \n\nIn this case, the maximum intensity loss constraint can guide the LDMs to solve this inverse problem, generating a fused image that does not contain Gaussian blur. This technique, which explicitly models the interference operator set and solves it using diffusion priors, has achieved remarkable results in the unsupervised image restoration field. (PSLD, NeurIPS 2023; LLIEDiff, AAAI 2025). We are the first to extend the aforementioned concept and apply it to address degradations in the multi-source image fusion process. Through careful design, we integrate the interference operator into various components of the MICP and LMG mechanisms, enabling our method to effectively handle challenging degradation scenarios such as low light, exposure issues, blur, and thermal diffusion. This results in significant improvements in metrics such as EI, SF, and AG.\n\n**Q6**: The advantages of our approach compared to the existing DDPM method.\n\n**Reply**: Compared to DDPM, LDMs offer a stronger generative prior, but their regularization constraints are more complex. The main contribution of our method is the design of an adaptive multi-source image regularization mechanism based on the generative prior of LDMs. To the best of our knowledge, this is the first **training-free**, **general**, and **robust** image fusion method that leverages pre-trained LDMs. Unlike existing CCF and DDFM methods, our approach is distinct in the following ways: **(1)** Our method leverages the stronger generative priors of LDMs. **(2)** We designed a more efficient MICP projection regularization mechanism, which decomposes the projection of $\\mathbf{z}_0^t$ into a multi-layer optimization of the coarse structure and fine detail layers, effectively alleviating gradient conflicts when optimizing multiple objective losses within the same layer. **(3)** The LMG mechanism we designed simultaneously applies regularization guidance in both pixel and latent spaces with multi-source images, effectively accommodating the many-to-one mapping of the VAE decoder in LDMs. Since the VAE decoder exhibits a many-to-one mapping, applying multi-source image guidance solely in pixel space results in multiple optimization directions for the latent space variable $\\mathbf{z}_0^t$. Introducing latent space regularization ensures that $\\mathbf{z}_0^t$ is optimized in a specific direction. **(4)** We designed a flexible alternating optimization strategy that balances multi-source information fidelity with the preservation of latent diffusion flow. **(5)** The regularization mechanism can naturally integrate the interference operator set, enabling it to handle various degradations in the multi-modal image fusion process, with strong robustness."}]

Author rebuttal:

Meta-review: This paper proposes a training-free image fusion framework built on a pre-trained latent diffusion model, incorporating two novel regularization mechanisms (MICP and LMG). The authors provided an exceptional rebuttal that comprehensively addressed the reviewers' concerns. Consequently, most reviewers were convinced and raised their final ratings. The paper received two "Accept," one "Borderline Accept," and one "Reject." The reviewer who gave "Reject" did not raise any specific or detailed follow-up questions in response to authors' replies but instead repeatedly reiterated concerns in a general manner. After carefully reviewing all comments, the rebuttal, and the discussion, the AC recommends acceptance.
Decision: Accept (poster)