Revisiting Generative Infrared and Visible Image Fusion Based on Human Cognitive Laws
Authors: Lin Guo
Xiaoqing Luo
Wei Xie
Zhancheng Zhang
Hui Li
Rui Wang
Zhenhua Feng
Xiaoning Song
Venue: NeurIPS 2025 spotlight ()
Keywords: Human cognitive law
Information bottleneck
Diffusion model
Infrared and visible image fusion
Physical law
Primary area: deep_learning
TL;DR: A generative infrared and visible image fusion method inspired by human cognitive laws combines a multi-scale variational bottleneck encoder and a diffusion model guided by physical laws to achieve superior structural consistency and detail quality.

Abstract: Existing infrared and visible image fusion methods often face the dilemma of balancing modal information. Generative fusion methods reconstruct fused images by learning from data distributions, but their generative capabilities remain limited. Moreover, the lack of interpretability in modal information selection further affects the reliability and consistency of fusion results in complex scenarios. This manuscript revisits the essence of generative image fusion under the inspiration of human cognitive laws and proposes a novel infrared and visible image fusion method, termed HCLFuse. First, HCLFuse investigates the quantification theory of information mapping in unsupervised fusion networks, which leads to the design of a multi-scale mask-regulated variational bottleneck encoder. This encoder applies posterior probability modeling and information decomposition to extract accurate and concise low-level modal information, thereby supporting the generation of high-fidelity structural details. Furthermore, the probabilistic generative capability of the diffusion model is integrated with physical laws, forming a time-varying physical guidance mechanism that adaptively regulates the generation process at different stages, thereby enhancing the ability of the model to perceive the intrinsic structure of data and reducing dependence on data quality. Experimental results show that the proposed method achieves state-of-the-art fusion performance in qualitative and quantitative evaluations across multiple datasets and significantly improves semantic segmentation metrics. This fully demonstrates the advantages of this generative image fusion method, drawing inspiration from human cognition, in enhancing structural consistency and detail quality.

Revisiting Generative Infrared and Visible Image
Fusion Based on Human Cognitive Laws
Lin Guo!
Xiaoqing Luo"
Wei Xie Zhancheng Zhang*
Hui Li!
Rui Wang' Zhenhua Feng' Xiaoning Song'
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China 2School of Electronic and Information Engineering
Suzhou University of Science and Technology, Suzhou, China fguolin, xiewei] jiangnan. edu. cn
{xqluo, lihui.cv, cs_wr, fengzhenghua, x. x.song)jj angganann..nn...mm edu. cn {zczhang) Gusts. edu.
Abstract
Existing infrared and visible image fusion methods often face the dilemma of balancing modal information. Generative fusion methods reconstruct fused images by learning from data distributions, but their generative capabilities remain lim- ited. Moreover, the lack of interpretability modal information selection further affects the reliability and consistency of fusion results in complex scenarios. This manuscript revisits the essence of generative image fusion under the inspiration of human cognitive laws and proposes novel infrared and visible image fusion method, termed HCLFuse. First, HCLFuse investigates the quantification theory information mapping in unsupervised fusion networks, which leads to the design of a multi-scale mask- regullted variational bottleneck encoder. This encoder applies posterior probability modeling and information decomposition extract accurate and concise low-level modal information, thereby supporting the generation high-fidelity structural details. Furthermore, the probabilistic generative capability of the diffusion model is integrated with physical laws, forming a time-varying physical guidance mechanism that adaptively regulates the generation process different stages, thereby enhancing the ability of the model to perceive the intrinsic structure of data and reducing dependence on data quality. Experimental results show that the proposed method achieves state-of-the-ar fusion performance in qualitative and quantitative evaluations across multiple datasets and significantly improves semantic segmentation metrics. This fully demonstrates the advantages of this generative image fusion method, drawing inspiration from human cognition, in enhancing structural consistency and detail quality. The source code is available at
Introduction
Infrared and visible image fusion has been extensively employed as a core technique in multi- modal sensing systems, which are widely adopted in surveillance, autonomous driving, and target tracking [ 22 3] Infrared sensors capture thermal radiation and are effective under low-light conditions, while visible sensors perform better in well-lit environments but degrade darkness or adverse weather. Image fusion aims to combine complementary advantages from both modalities, traditionally formulated as a deterministic mapping based on handcrafted features, using techniques like multi-scale decomposition 4 5,,55, 6 or sparse representation 7 8 19. Although these methods
are computationally efficient, they lack the ability to capture semantic relationships and handle uncertainty, which often results in suboptimal integration of multi-modal information. Recent deep learning-basee approaches adopt data-driven strategies to automatically learn cross-modal
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Infrared
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Valla
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Dos
Indox
Source Imsges
Visualization Coussian Curvature
Coussian Curvature Distribucion
Figure I: Comparative visualization of gaussian curvature in generative infrared and visible image fusion methods.
relationships, substantially improving fusion quality. Generative models further advance this by modeling the task as a conditional distribution p(z z, y). capable of handling uncertainty and capturing deeper modality interactions. However, existing generative methods still face several limitations:
Limited generative capability: Existing methods often focus feature extraction (representative methods: Dif-Fusion [0]], LFDTT-Fusion II] or optimization (representative methods: Diff-IF 12)), failing to fully leverage the potential of generative models.
Lack interpretability: Current methods lack sufficient interpretability regarding how information from different modalities is selected and processed during fusion.
Strong data dependence: These models heavily rely on statistical distribution properties and lack deep understanding of the intrinsic rules of the modalities. As result, their robustness is weak when confronted with data distribution shifts noise interference.
To visualize these limitations, Gaussian curvature is introduced as a geometric indicator of structural consistency. As shown in Fig. bb). existing diffusion- methods exhibit fragmented curvature patterns in critical regions, suggesting incoherent structure retention and biased information integra- tion. This indicates that the methods fail achieve optimal information selection and retention in the integration of modal information. Existing generative models focus primarily on data distribution, neglecting the intrinsic understanding of the data. In contrast, human cognition inspires us combine empirical data with abstract reasoming and domain knowledge. As highlighted by Tenenbaum et al. [13, mechanisms such selective attention and physical laws play crucial role in guiding robust perception under uncertainty, which remains largely absent in current fusion models. Selective attention involves prioritizing task-relevant information while disregarding redundant or irrelevant inputs. Adherence to physical laws involves integrating perceptual input with domain knowledge to support robust inference.
This paper revisits generative infrared and visible image fusion through cognition-inspired modeling principles and proposes novel method. The framework(see Fig. aa)) integrates data-driven generative model with theorem-constrained probabilistic reasoning, enabling more interpretable and robust fusion. A multi-scale variational bottleneck encoder is designed to extract structured low-level features through information quantization, which are then guided by a physics-aware diffusion process. By incorporating physical laws into the generative trajectory, HCLFuse enhances semantic consistency and reduces reliance on high-quality data. As shown in Fig. [ HCLFuse not only demonstrates more complete structural features in curvature visualization but also surpasses the curvature quality of the original images in certain regions. Overall, the main contributions of this paper are summarized as follows:
A novel generative fusion framework proposed to enhance modality interpretability and structural consistency by incorporating human cognitive laws.
multi-scale variational bottleneck encoder is introduced to extract discriminative low-level representations through unsupervised information mapping quantization theory.
The generative ability of the diffusion model is combined with physical laws to form time-varying physical guidance mechanism, enhancing the ability of the model to perceive the intrinsic nature of data, reducing data dependence.

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Extensive experiments demonstrate superior performance in both fusion quality and downstream
semantic segmentation.
2 Related Work
2.1 Deep Learning-Band Based Fusion Methods
With the advancement of deep learning, image fusion has evolved from traditional deterministic models to data lata-driven approaches capable of capturing complex modality relationships. Early methods such as DenseFuse [14] introduced convolutional encoders with dual fusion strategies, while NestFuse[15] enhanced detail preservation via multi-scale nested connections. To improve interpretability, [RRNet(16] employed lightweight architecture that approximates optimal fusion solutions, and [ incorporated masked attention mechanisms into a general two-stage fusion pipeline. Recent trends favor end-to-end architectures. PMG unified diverse fusion objectives under common optimization formulation, and STDFusionNet [1] leveraged salient target masks to jointly model detection and fusion. Transformer- based methods further expanded global context modeling: SwinFusion [20] designed intra- and inter-domain modules based on the Swin Transformer; SegMiF[21 applied hierarchical attention for fine- nne-grained representation; STFNet 22 focused pixel-level dependencies to mitigate ghosting; and CrossFuse (23] proposed complementary attention to suppress modality-specin
redundancy. While these methods have improved cross-modar modal feature integration, they often treat fusion deterministic overlay, overlooking its generative nature. Consequently, their performance is limited when handling degraded inputs or incomplete modality information, restricting the semantic expressiveness of fused outputs.
2.2 Generative Models for Image Fusion
With the evolution of generative models, image fusion task has gradually been formulated within the framework of Generative Adversarial Networks (GAN). FusionGAN [24] was among the earliest attempts to establish an adversarial learning scheme between generator and discriminator, wherein the generator synthesized fused image and the discriminator evaluated the detail-level differences between the fused image and the visible image. This setup was intended improve the structural integrity and perceptual realism of the fused result. The application of GAN in image fusion is hindered by inherent limitations, including training instability and mode collapse, which reduce both generalization ability and generation quality. To address these limitations, diffusion models(DM) have been introduced
compelling alternative, offering advantages such as progressive generation, high fidelity, and stable training dynamics. Dif-Fusion[10 employed a diffusion model as a feature extractor to guide the fusion process and produced fused images with improved color fidelity. Diff- [F incorporated prior knowledge of the fusion task to condition the diffusion model, enabling the generation of high-quality fused image even the absence of ground-truth CCF(25) proposed conditionally controllable fusion framework that relaxed the constraints imposed by fixed fusion paradigms and improved the adaptability and generalization of the generation process.
Although diffusion- based image fusion methods have demonstrated notable advances in both per- formance and representational capability, most existing approaches continue rely heavily on conditioning from data distribution. Therefore, the full potential of diffusion models as generative mechanisms for image fusion remains underexplored. This work establishes a theoretical framework for unsupervised image fusion from the perspective of human cognitive principles. Furthermore, vire-varying physical guidance mechanism is developed by integrating physical laws with data-driven distributions, aiming to generate fused images with improved structural consistency and information completeness.
3 Method
3.1 Problem Statement and Modeling
In HCLFuse, let the infrared image domain denoted as and visible image domain as V. with joint observations (x, v) Pr.y- A fusion mapping F²) + Z is constructed to generate fused latent representation 2 = F(z, y) E Z, where Z C 12d denotes the fusion space. The detailed
3

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phys con
Eacodler
Physkal
Lews
Wa(Pe. pr) pa.PP
Generation Decoder
Informnce
( Ci 2000
Protebility
Dntribation
Za
VBE
Decader
Sclective Attertionn
Z
Diflusion Timestop
t=T
0
X
4
Foual Image
(4) Overall framework
(b)
Across Diffusion Timesteps
Figure 2: Overall architecture of HCLFuse and feature evolution across the diffusion process.
architecture and optimization workflow of HCLFuse are provided in Appendix B Ideally, the fused representation is expected to effectively preserve complementary information from both modalities while suppressing redundancy and noise. The image fusion task formulated as trade-off between compressive sensing and information preservation, aiming to optimize the selectivity of information to extract the most discriminative features. Specifically, assuming the fused representation follows a conditional distribution Z ~ ((2|1x, y). the optimization objective is defined as the maximization of the joint mutual information between and the modal inputs:
max X,Y; ((=|a,y)
Here, the mutual information I(Z; X, Y) quantifies the amount of information from the modal inputs preserved in the fused representation (26), and is defined as follows:
I(2:X,Y) /ʃ 9(2,,, (0,,I,)) log g(2|2, y) dz dx dy p(z)
(2)
However, directly maximizing the information quantity may lead to the retention excessive redundant content, which contradicts the seleetive attention mechanism observed in human cogmitive systems. Within the predictive brain framework proposed by Clark cognition interpreted as an active process that adjusts internal models by minimizing prediction errors, thereby emphasizing the necessity of focusing on task-relevant information. To address this issue, the information bottleneck (IB) theory is introduced mechanism to regulate information flow. The IB principle seeks balance between compression and preservation by maximizing the mutual information between the fused representation and task-relevant variable C, while minimizing its mutual information with the input:
max I(Z:C) -I) q(=|2.9)
Here, serves trade-off coefficient that controls the balance between task relevance and com- pression. In unsupervised image fusion scenarios, explicit labels C' are unavailable. Therefore, proxy task designed by leveraging the modality alignment capability as a surrogate measure of task relevance.
Theorem 1. (Lower Bound of Mutual Information under Unsupervised Mapping) Let modal inputs X ~ px and Y ~ Py, with the fused representation Z ~ q(2|11, y). Assume the existence of latent task-relevant variable C that satisfies the causal dependency C C+ (X,Y) + Z. Then, there exists an optimal transport mapping (29] T : + X², such that the fused representation Z generated from the transformed X' T"=T" T (X) and Y satisfies the following lower bound on mutual information (the full derivation is provided Appendix A.T].
I(Z:C) 2 I(Z;X',) - 2 I(Z;X,)) a -Wa(xxx) # Px Py))
where > 0 denotes upper bound on the residual task-irrelevant information, 0 is sensitivity factor, W2(..) represents the second-order Wasserstein distance, and # denotes the pushforward distribution of px under the optimal mapping T This establishes principled

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foundation for fusion optimization through transport-based modality alignment. It reveals that the improvement in [Z; C*) is lower bounded by the reduction in Wasserstein distance after applying the optimal transport map 7 offering a quantifiable and optimizable surrogate objective for information alignment under unsupervised conditions.
3.2
Variational Bottleneck Encoder
Under the guidance of the optimal transport mapping proposed in Theorem | a variational bottleneck encoder (VBE) is designed to extract salient and compact representations under information alignment. The input to the encoder consists of the concatenated transformed infrared image X' and the visible image Y. Only the infrared image transformed according to an optimal transport plan, while the visible image remains unchanged to ensure stability and efficiency. The transformation operator T obtained by multiplying the optimal transport plan P with the flattened infrared tensor Xua E IR BB where N = H x W:
(x) P
Xiai: p-= arg min
E + Σ
Pay log Pij-
PEU((r
Here, U(r,e) c) denotes the set of doubly stochastic matrices with row and column marginals r and Ci. represents the squared Euclidean distance between flattened pixel values of infrared and visible images, and E is a regularization coefficient. Through this transformation, the infrared image is geometrically and semantically aligned the visible modality, reducing structural discrepancy between modalíties and improving training efficiency. The transformed infrared image X' = (X) and the original visible image Y are then jointly encoded model their latent representation as Z ~ q( YY). The optimization objective of the VBE is formulated as:
LVBE
[x (lgg [2) (oo
+ 3 DxL (ZX,)
(6)
The first two terms evaluate the reconstruction capability of Z with respect Y and X', while the third term imposes a Kullback-Leible -eibler (KL) divergence regularization to constrain the posterior (( Y) from deviating significantly from the prior p(2), thereby enabling controllable compression. The parameter 3 > 0 governs the strength of this information bottleneck constraint. The objective LvBE essentially unifies the modeling principles of the Variational Autoencoder and the Information Bottleneck framework. ensures robust cross- modal reconstruction while compressing redundant information in the latent space, thus generating structurally consistent and semantically compact fused representations Z for the subsequent conditional modeling stage of the diffusion model. Under this bottleneck constraint, a multi-scale masking mechanism is further introduced adaptively filter features at different scales, serving as a complementary enhancement rather than the bottleneck itself, therehy enhancing the expressiveness of the latent representation Z. This process is formulated
F, = concat(X', (X', Y). Fm = a (0, (M, @ F.))
where (.) denotes the activation function, 0,, represents learnable parameters, and M, denotes the mask weights that determine the importance of each feature, which are learnable parameters obtained through differentiable transformation M2- = (igmoid(w.). where w, E RIxxxxxx is initialized from a normal distribution and jointly optimized with LVBE. F, refers to the input of the VBE module. The operator @ indicates element-wise multiplication. through which the mask M, is applied to the features F. to obtain the condensed key feature representation Fm. To characterize the distributional properties of critical and uncertain information within the latent representation Z more explicitly, the posterior distribution 9(Z F,,. is modeled Gaussian distribution. Due to its continuity and differentiability. the Gaussian distribution facilitates sampling and optimization within the variational inference framework. Meanwhile, its parameterized structure enables effective modeling of both deterministic and uncertain components in the latent space. This modeling is expressed as:
q(Z(Fm) (H,
(8)
The mean f and variance o² of the latent variable Z are computed from the masked features Fm, to represent the deterministic and uncertain components, respectively. This design enables a controllable generative capacity in the latent space while preserving discrininative semantic features from the multi-modal input, thereby facilitating improved expressiveness and structural consistency in the subsequent diffusion model. To further characterize the information structure within the latent
5

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representation, the latent variable Z ~ N(u.03) output by the variational bottleneck encoder is structurally decomposed. Specifically, considering that the encoder output contains both deterministic information driven by the input features and stochastic perturbations introduced by the variational modeling, the latent variable Z can be expressed as:
Z = pa + R, R~ N0,,0")
where μ denotes the mean vector computed from the masked features Fm. representing task-relevant structural information, and I denotes zero-mean Gaussian perturbation with covariance o², model- ing the uncertainty introduced during the information bottleneck compression process.
Theorem (Upper Bound of Redundant Mutual Information in the Perturbation Term). Based on the decomposition in (9), consider the mutual information between the perturbation term R. and the task malk-releantt component p. Assuming a heteroscedastic Gaussian reparameterizatioo consistent with the encoder implementation.
R=OOE, I,,
Under the leint-Gaussian and channel-diagonee dominance assumptions (see Appendix A.2 the redundant mutual information admits the following upper bound:
1 2 Σ log (1- Var[us) of
I(R;M)
(10)
The latent variables decoupled by the variational bottleneck encoder provide critical features for the diffusion model. By constraining the redundancy in R. the diffusion process focuses on task-relevant structures, thereby improving the quality and consistency of the generated results. As shown in the first row of Fig. (2)),, the encoder output gradually captures refined and discriminative structural information, which is then fed into the subsequent generation and reconstruction processes.
3.3 Physics-Guided Conditional Diffusion Model
Diffusion models synthesize images via reverse denoising processes, capturing distributional patterns from large-scale data. Inspired by the interplay between empirical learning and physical reasoning in human cognition, a physics-g guiidd conditional diffusion model is proposed, in which data-driven estimation is integrated with physically grounded constraints derived from domain knowledge. This hybrid mechanism enhances generalizability and interpretability while reducing dependence high-quality data. basic diffusion models, the reverse process (denoising sampling) is entirely based on the learned conditional distribution, typically formulated as:
po 1 2x) NN (14(221) 1), Eo 1))
(11)
To reinforce structural consistency and physical interpretability, the proposed physics-guided diffusion model introduces a physically grounded correction term Ayphys () into the reverse process, with the VBE-generated latent Z input. The modified sampling is defined as:
phys (=.-1 zx) R N (uo(ze,)) () + ( l), ()(,,()) Po
(12)
where Apppys (Ze, t) denotes physics-based correction term that guides the generation process to obey fundamental physical laws. The probabilistic model provides an experience-driven initial estimate, while the physical constraints serve rule-based corrections.jointe process akin to the human cognitive laws between empirical experience and physical theorems. At each diffusion step generation proceeds in two stages: (1) a probabilistic estimate 2n is computed via denoising; (2) a physics- based correction sphys T physiccs( (2o, t) is applied, followed by reverse sampling:
forming generation
2 Va-- phys + VI a1-1 phys
(13)
Where e plys represents the re-estimated noise corresponding to :pbys ensuring consistency both semantic structure and noise components. This two-stage reasoning is executed at every timestep, enabling the generative trajectory to maintain data-driven capabilities while incorporating structural and physical corrections, leading more realistic, stable, and physically plausible outputs. Under

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the proposed sesssiegggieeed framework, three types of physical constraints are introduced to capture the intrinsic physical laws relevant to infrared and visible image fusion:
Physics (Zo, 1) Dcon Saa Prea (2o; 1),))
(14)
Heat Conduction Constraint. This constraint reflects the law of energy transfer in physical materials, modeled on thermodynamic heat conduction. It describes the spatial diffusion of thermal energy across object surfaces, enforcing smooth and physically plausible energy distributions within homogeneous regions. The constraint is defined as:
Deeat (2o, t) = Eo + (ncar(t) 80
(15)
where V denotes the Laplacian operator, and Anear (t) is the time-dependent heat diffusion coefficient. This constraint encourages the generated image to follow the heat conduction equation at a VVu thereby suppressing artifacts and discontinuities inconsistent with thermal physics.
Structure Preservation Constraint. Based the assumption that object boundaries and structural features remain stable over short temporal intervals, this constraint is designed to maintain edge sharpness and shape consistency during fusion. It is formulated as:
stu ( (zbe.t ,t) = 20 + )tr (Gmax G M
(16)
Where Ginx denotes the maximum gradient map of the source image pair, providing structural edge information. G Gages is the gradient of the current estimate. And Maru is structural mask derived from the high-frequency responses of the visible image, indicating important structural regions. This constraint enforces structural similarity to the source, ensuring the physical stability of prominent object boundaries.
Physical Consistency Constraint. This constraint enhances roos- physical coherence, ensur- ing that both modalities depict the object consistently in terms of physical properties. It is defined as:
con [sstm t) 20
20Th + (to (wie X Mheat + Weis Y Maru);
(17)
Where Mheat is the mask derived from the thermal-intensity distribution of the infrared image.Both masks are non-learnable spatial physical priors that guide the diffusion process toward physically plausible and cross- mods-lyy consistent regions. The weights Wi and Wivs control the contributions of the infrared and visible modalities, respectively. To adapt to the varying uncertainty and mitigate potential bias introduced by imperfect masks during the diffusion process, Time-varying Physical Guidance (TPG) mechanism is introduced, which defines each of the above (t(f) as:
(e(1) = XN -
(18)
where Xy is the initial constraint weight, is a decay factor, and f is the normalized timestep. This mechanism reflects the cognitive process of "coarse perception followed by fine reasoning": in early steps (high noise, high uncertainty), stronger physical constraints provide guidance. while in later steps (low noise, high certainty), the model relies more on learned semantics and structural details for restoration. By leveraging this mechanism, the model dynamically adjusts physical guidance intensity, enabling robust and physically grounded cross-modal generation. As illustrated in Fig. (]b), the visualized feature maps different timesteps clearly exhibit this transition from noisy coarse perception to refined semantic restoration.
4 Experiments
4.1
Experimental Setup
and MFNet [2], covering diverse conditions such as urban driving, nighttime military scenes, and adverse weather. Seventeen representative fusion methods are selected for comparison. Quantitative evaluations are performed using seven no-reference and five reference-based metrics. All experiments are implemented on an NVIDIA RTX 3090 GPU. Detailed descriptions of datasets, competing methods, hardware, and evaluation metries are provided in Appendix C.I
HCLFuse is evaluated on four public datasets: MSRS 30) TNO BI, FMB 21
7

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(a) Visible
(b) Infrared
(c) FusionGAN
(d) NostFuse
(c) SwinFusion
(1) TarDAL
(g) SegMiFF
(h) SOSMusFFuse
① LRRNet
i STFNet
(k) CrussFuse
(3) DDFM
(m) DaIr-IF
(n) Text IF
(o) CCF
(p) Text-DiFuse
(q) MMAE
(1) LFDT-Fusion
(s) Gll-Net
(1 Ours
Figure 3: Visualization results of several methods on MSRS dataset 00621D (image name) scene.
Table 1: The quantitative metrics of various algorithms in MSRS dataset. Bold indicates the best result. underline indicates the second-best result.
Method
SDt
AGf
CC1
SCD1
ENT
SFT
Nabfß
DP
QSFt
VIFT
PIQEL
BRI
FusionGAN
19.644
1.6646
0.6276
1.0763
5,5537
5.0135
0.0239
1.9605
-0.5557
0.4627
44.120
35.543
NestFuse
46.141
3.5573
0.5941
1.5632
6.1205
11.616
0.0112
4.0834
-0.0207
0.9099
41.856
37.513
SwinFusion
47.651
3,7885
0.5900
1,5815
6.0590
12,550
0.0095
4.3502
0.0651
0.9119
38.137
37.551
TarDA
35.460
3,1149
0.6261
1.4837
6,3476
9.8729
0.0098
3.8817
-0.1419
0.6728
22.898
26.165
SegMiF
40.351
3.0449
0.6130
1,5482
6.3297
9.4273
0.0177
3.4624
-0.1968
0.6239
42.081
34.445
SOSMaskFuse
45.647
3,3081
0.5492
1,2552
5.8463
11.248
0.0143
3.7733
-0.0457
0.8585
44.769
40.064
LRRNet
36.849
3.0502
0.5171
0.8356
6.3341
9.8093
0.0208
3,6042
-0.1617
0.5680
29.963
30.822
STFNet
46.980
3.3167
0.5992
1.5931
6,3750
9,9802
0.0113
3.6135
-0.1389
0.8463
$4.532
43.027
CrossFuse
36.309
3.0084
0.5433
1,0533
6,4947
9.6134
0.0243
3.5304
-0.1805
0.8374
33,084
33.594
DDFM
28.923
2.5219
0.6585
1.4493
6.1748
7.3879
0.0196
2.9599
-0.3581
0.74291
37.389
35.674
Dif'-IF
42.598
3,7100
0.6023
1.6243
6.6686
11.460
0.0087
4.3206
-0.0237
1.0417
32.734
31.515
Text-IF
44,588
3.8811
0,5982
1.6976
6.7280
11.879
0.0075
4.5075
0.0165
1.0506
33.987
31.681
CCF
28.946
2.8753
0.6495
1.4087
6.1917
9.0417
0.0154
3.6091
-0.2369
0.6847
17.338
26.380
Text-DiFuse
54.243
3.7000
0.5710
1,3816
7.1436
11.408
0.0125
4.1898
-0.0168
0.73116
35.283
34.988
MMAE
41.938
3.5325
0.6034
1.4173
6.1731
12,839
0.0087
4.1595
0.0719
0.8090
33.176
31.247
LFDT-Fussin Fusion
43.052
3.6429
0.6003
1.6370
6.6504
11.230
0.0095
4.1986
-0.0422
1.0296
38.861
32.374
GIfNeet
32.901
3.3673
0.6278
1,4082
5,9404
12.705
0.0110
3,8030
0.0654
0.5823
43.581
38.136
Ours
49.546
6.4355
0.6186
1.6575
6.8704
17.899
0.0017
7.6427
0.5374
0.8540
25.193
26.215
4.2
Qualitative Comparisons
Fig. and Fig. Fig.-iill Aillustrate the fusion results of various methods on the MSRS dataset under daytime and nighttime scenes, respectively. As shown in Fig. [3] the red bounding box highlights pedestrian targets that are prominently captured in the infrared modality. Several methods, including FusionGAN, SOSMaskFuse, CrossFuse, and CCF, tend to over-enhance the thermal response, resulting unnatural brightness distributions and noticeable visual artifacts. Additionally, MMAE fails to preserve the sign within the green box, leading to a critical loss of structural information an example of severe fusion emoor In contrast, only the proposed FCL.Fuse successfully preserves complementary features from both modalities, while also demonstrating degree of detail restoration. For instance, fine details such the bicycle wheels, leaves in the background, and pavement textures clearly visible, indicating

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(a) Visible
(b) Infrared
(c) FusionGAN
(d) NcstFuse
(c) SwinFusion
i TarDAL
(g) SegMiFF
(h) SOSMasFFuse
① LRRNet
i STFNet
(k) CrussFuse
(1 DDFM
(mn) DaIT-IF
(x) Text- IF
(o) CCF
(p) Text-DiFuse
(q) MMAE
(1) LFDD--Fusion
(6) GllNNet
Ours
Figure 4: Visualization results of several methods on MSRS dataset 00774N scene.
better semantic preservation and structural coherence. As shown in Fig. 4 the scene is captured under low-light conditions, where infrared saliency becomes particularly important. However, methods such as LRRNet, CrossFuse and GIFNet fail to maintain the thermal prominence of pedestrians the red box, thereby compromising target visibility. From a global perspective, TarDAL, Text- DiFuse, and HCLFuse preserve structural information in the green-box region, while only our method maintains higher perceptual resolution with clearer details. These results are generated by effectively integrating visible and infrared sources while maintaining structural integrity, thereby achieving superior perceptual quality without deviating from the underlying content.
4.3
Quantitative Comparisons
Table reports the quantitative results of all compared methods on the MSRS dataset. The proposed HCLFuse achieves superior performance most metrics, particularly excelling in texture clarity and structural fidelity. In terms perceptual sharpness, HCL.uuse obtains the highest AG, outperforming the second-best method by 69.87%, and reaches the best SF with a 39.41 % relative gain, reflecting its strong capability preserving fine-grained details. For DF, HCLFuse improves upon the next best result by 65.56%. indicating significantly enhanced visual clarity. Notably, HCLFuse achieves a substantially higher QSF score compared to competing methods, demonstrating its superior capability in preserving directionally distributed frequency information. In addition, HCLFuse attains the highest EN, confirming its ability to maintain information richness while suppressing unnatural responses.
4.4 Generalization Evaluation
To further verify the robustness and generalization ability of HCLFuse across diverse datasets and scenarios, additional comparative experiments are conducted on the TNO, FMB, and MFNet datasets, with detailed quantitative and qualitative results presented in Appendix C HCLFusee consistently outperforms existing fusion methods by leveraging human cognition-inspired generative capability, while simultancously maintaining strong generalization and robustness under varying conditions.

---

4.5 Downstream Task Evaluation
The ultimate goal of image fusion is to enhance the performance of downstream vision tasks. Among them, semantic segmentation places particularly strict demands on fine-graines semantic details. To evaluate this aspect, comparative experiments are conducted the MSRS dataset using the Mask2Former [33 framework. As shown in Appendix [C.6] HCLFuse achieves superior segmenta- tion performance, attributed to its ability to retain fine structural and semantic cues, consistently outperforming other fusion baselines in this challenging downstream setting.
4.6
Ablation Studies
To evaluate the effectiveness of each component in HCLFuse, ablation experiments are conducted using the same quantitative metrics. shown in Table the complete model achieves the best performance on most indicators. In W/O TPG, the physics-guided constraint is removed, and sampling performed purely through data-driven diffusion. While CC and Nabf show slight improvements. most metrics drop significantly, indicating unstable generation without physical priors. In W/O VBE, the VBE replaced with a standard multi-scale encoder. Although this variant ranks second overall, visual artifacts such as coarse building textures and unnatural sky transitions appear (see Fig. 55) reflecting the model's reduced ability filter and synthesize relevant features. W/O OT, the optimal transport module proposed in Theorem iis removed, resulting in sharp declines across all metrics. This highlights the necessity distribution alignment between modalities for stable fusion. In W/O DDIM, the deterministic diffusion sampling module (DDIM (34) is disabled, which degrades both quantitative scores and visual quality. This confirms the critical role of the diffusion process in generating coherent fused images. More detailed ablation studies are presented in Appendix C.7 further illustrate the effectiveness the proposed method.
Table Quantitative comparison of fusion performance in ablation studies on the effectiveness of designed modules. Bold indicates the best result. underline indicates the second-best result.
DDIM OT VBE TPG SDt AG
CCt
SCD*
EN1
SFt
Nabf].
QSF
VIFI
PIQE.
BRI.
W/O TPG
36.90
5.521 0.646
1.595
6.495
15.158
0.0015
6.513
0.303
0.738
22.21
33.54
WO VBB
4
42.68
6.038
0.607
1.521
6.736
17.259
0.0018
7,838
0.459
0.804
23.07
32.69
W/O OT
X x
28.66
3,578
0.629
1.322
6.188
11.090
0.0107
4.391
-0.058
0.734
23.97
29.85
W/O DDIM
28.36
3.626
0.635
1.337
6.176
11.218
0.0099
4.386
-0.047
0.741
26.77
32.08
Ours
49.55
6,436
(1.199
1.658
6.870
17,899
0.0017
7.643
0.537
0.854
25.19
26.22
(a) Visible
(b) Infrared
(c) W/O DDIM
(d) W/O OT
(c) W/O TPG
(f) W/O VBE
Ows
Figure 5: Visualization of ablation study results on the MSRS dataset.
5 Conclusion
novel generative fusion framework is proposed by revisiting infrared and visible image fusion through the lens of human cognitive laws. Existing generative methods often lack modality inter- pretability and exhibit weak generative capability, To resolve these problems, multi-scale mask- modulated variational bottleneck encoder grounded in information mapping theory is developed. This encoder enables accurate extraction of low-level modal cues, which significantly enhance structural fidelity during generation. Furthermore, physical laws are incorporated into the diffusion process to form time-varying physical guidance mechanism, which enhances the model capacity to perceive intrinsic data structures and reduces dependence on data quality. HCLFuse achieves strong perfor- mance across various benchmarks. However, its reliance well-aligned infrared and visible image pairs, together with the computational overhead introduced by the diffusion process, may limit its applicability in real-time resource-constrainee scenarios.
10

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Acknowledgement
This work supported in part by the National Key Research and Development Program of China under Grant (2023 105102, 2023YFFI 1223FFI1105005 the National Natural Science Foundation of China under Grant 61772237, the Joint Fund of Ministry of Education for Equipment Pre-research under Grant 809 1U91204233.
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Appendix
A Proof
A.1 Proof of Theorem 1
In the absence of explicit labels, the task-relevant variable C' is unobservable, which makes the direct optimization of [[Z;C) [Z; X, Y) intractable. We therefore derive computable lower bound on I(Z; C)) by aligning modal distributions via optimal transport.
A.1.1 Information Inequality from the Causal Structure
Given the causal Markov condition C + (X - Z, we have the conditional data-processing identity:
((;C)- = I(2;X,Y) - I(2; X,Y > I(Z;X,Y) -
(19)
where E > is an upper bound on the residual task-irrelevant information conditioned on C. Let
T X >X be measurable map and set X' 32-770 =TX). Since X-X' (Z Y) is Markov chain:
I(Z:X,Y) < I(Z:X,Y)
(20)
Combining 19 with 20 yields:
I(2C)) 2 Y) -
(21)
A.1.2 Effect of Distributional Transformation
View I(Z:X', H(Z) H(Z X',Y) functional of the forward marginal
Px = T#px. Assume the conditional negative log-likelihood (decoder) log g(= æ,,) is L- Lipschitz (r, v). By the Kantorovich-Ruuinseer duality, there exists T' such that
H(Z X.,Y) H(Z X,Y) & Wi(x. T px).
(22)
Using Wi S W2 yields
H X' H(Z X,Y) S L'WApxx Px)x)
(23)
Moreover, we consider T chosen along the W₂ displacement interpolation from px to py (i.e., T Tr with [0, 1] the McCann geodesic induced by the OT map), for which the metric projection satisfies
Wa(px, px) = Ws(px.Pr Ws(T* px.Pr)
(24)
Combining 23 and (4). and absorbing constants into a >> we obtain
H X',Y H(Z X,)) s a Ws(px.Pr) Ws(T px.T))
(25)
Hence, for any such T along the geodesic we have
I(2:X',Y) 2 I(Z:X,Y) - a Ws(px.Pr) Wx(T#px.Pr)
(26)
A.1.3 Optimal Transport Map and Final Bound
Define the optimal transport map by:
T arg min Wx( ( px py)
(27)
For T = T we have W2(T px px px) py) & W2(px,pr), so the difference is non-negative and 226
gives:
I(Z:X',Y) I(2:,,Y)
We(px.py) " px) with X' = (X) (28)
Finally, combining 21 and 28
yields the two-step chain in Theorem
I(Z:C) 2 I(2;X',) - I(Z;X,Y) a. ".[W(Px.P) - WT(T px
(29)
14

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A.2 Proof of Theorem 2
We consider [R:: pi) with R= & and NN(,,)) I). Let En Cov(p), Σn = Cov( R) (diagonal, with entries of understood the batch+spatial mean per channel), and Σn,, Cov R, jt). Under the joint- Joint-Guussian assumption, the mutual information admits the Schur-complement form:
[R Enlpl
I(R: pD) log
ERIn ER
LR,
(30)
Define
M Ex 1/2 En. En En En Ex/ 0.
(31)
Then 330 can be rewritten
1 log det det((1 M) 1) - 2
1
I(R:
log det I M).
(32)
To obtain a computable and conservative upper bound, we adopt a channcl-diagonal dominance approximation,
M 3 D dii..dd................ da); di ER Sp,
Var[us]
(33)
of
of
By Loewner order monotonicity, (I 1--M)- (I-D)-1 hence
det (TT s det ((T D)-1)
(34)
i=1 dg
Taking logarithm and using di Varluil/ yields
1 2
log (1- Varluu)
I(R: S 2
log(1 - di) S
(35)
of
B
Algorithm
HCLFuse first applies an traspo--ra mapping T to the infrared image X, aligning its distribution with that of the visible image Y and thereby improving the optimization lower bound of the mutual-information objective. The aligned pair (T*(X), Y) is then fed into multi-scale, mask-regulated variational bottleneck encoder (VBE) to compress and model the latent representation z, so that = captures modality-disg disrriiiddisr and compact features under unsupervised learning setting. Subsequently. is refined through a reverse-time diffusion generation process, in which physically guided constraints are dynamically injected each denoising timestep to regulate the evolution of latent features. Finally, the optimized latent representation 2g is decoded to produce the fused image F. The pseudocode implementations of both the training and inference procedures are provided in Algorithm and Algorithm 2 respectively.
C Experimental Results
C.1 Experimental Details
Datasets. To comprehensively assess the fusion performance of HCL.uue method, three publicly available datasets are utilized: MSRS(30) TNO[3]] FMB (2I), and MFNet [32). The MSRS dataset provides 1,444 co-registered infrared and visible image pairs, primarily depicting urban driving scenes under both daytime and nighttime conditions. The TNO dataset contains 80 multispectral image pairs focused on nighttime military applications. FMB offers 1,500 aligned defrared-visibee image pairs, covering broad range of scenarios and illumination settings. Lastly, the MFNet dataset contains 1, 1,569 pairs of co-registered RGB and thermal infrared images, captured in urban driving scenes under both daytime and nighttime conditions. In the experiments, a subset is sampled to ensure diversity and representative coverage: 361 pairs are selected from MSRS, 42 pairs from TNO, 280 pairs from FMB, and 393 pairs from MFNet. These selected subsets are used to validate the generalization capability of HCLFuse across varying scenes and lighting conditions.
15

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Algorithm Training
Input: Source images X and Y, total diffusion steps T Output: Trained noise predictor eg and fused image F
1: for epoch = 1 to epochs do
2: X' t (X)
3: 2 + VBE(concat( [X', Y))
4: Sample Uniform( ((1 (1....,7]) TT)
5: Sample (0, I)
6:
for -.T-1...... do do
7:
Eø < NoisePredictorr(aa
8:
Update (1( using Eqs. (17-18)
Calculate the pbys using Eqs. (14-16)
9:
Calculate the 2r-1 using Eq. (13)
F < -Decoder(=) (=)
11:
12:
Update Ea using training loss
Algorithm 2 Inference
Input: Source images X and Y, total diffusion steps T
Output: Fused image F
1: < VBE(connat/ (X, Y))
2: Sample 2T ~ (O.
3: for f. for
4: Eg < Noiieerrediccto((2,
5: Update (t) using Eqs. (17-18)
6: Calculate the 20 phys using Eqs. (14-16)
7: Calculate the 21-1 using Eq. (13)
8: F Decoder (2)
phys)
Competing Methods. To comprehensively assess the effectiveness and robustness of HCLFuse method, comparisons are conducted against seventeen state-of-the oh-the-rrt image fusion methods. These include three non end-to-end methods: NestFuse [5], LRRNe([16), and MMAE[1]; seven end-
to-end learning-based methods: SwinFusion 20], 2e1 SOSMaskFuse (35) STFNet 22
CrossFuse 23,, Text-IFF (36] and GIFNet 37]; ; as well as seven generative approaches: FusionGAN 24
TarDAL 33), DDFM (39) Diff-IF 12, CCF(25), Text-DiFuse (40), and LFDDT-Fusion (TI] All experi- mental evaluations
performed on computational platform equipped with NVIDIA GeForce RTX 3090 GPU and an Intel(R) Corc(TM) 17-6850K CPU operating at 3.60 GHz. The Adam optimizer with learning rate of x 10- is used for parameter updates.
Metrics. To quantitatively evaluate the fusion performance of HCLFuse, twelve metrics are adopted, consisting of seven no-reference indicators and five reference- barence--bsed measures. The no-reference metrics include standard deviation (SD), average gradient (AG) entropy (EN) 42 spatial frequency (SF) 43), definition (DF), perception-based image quality evaluator(PIQE)) blind/referenceless image spatial quality evaluator (BRISQUE, abbreviated as BRI.)
and 45 The
reference-basee based metrics comprise the correlation coefficient (CC), the modified fusion artifacts measure (Nabf), the sum of correlations of differences (46)) the quality via spatial frequency (QSF) [47. and the visual information fidelity (VIF)
C.2 Comparison on TNO dataset
Qualitative Evaluation. Fig. and Fig. present visual comparisons between HCLFuse and 17 existing fusion methods on the TNO dataset. As military-focused benchmark, TNO emphasizes the preservation of thermally salient targets under low-illumination conditions. In Fig. ((c). (f), (k), and (s), the thermal prominence of soldiers within the red box is noticeably suppressed by most baseline methods. In contrast, HCLFuse preserves high-contrast thermal features and structural detail, particularly in critical regions such as weapons and head contours, which appear more distinguishable from the background. In addition, surface textures- as roof tiles are reconstructed with enhanced clarity, indicating the generative capacity of the proposed diffusion-
16

---

based model in recovering fine-gr grained visual information. Similar superiority is observed in Fig. 7 where both target saliency and detail sharpness are consistently maintained across complex nightfime scenarios. Overall, the results produced by HCLFuue achieve compelling balance between structural integrity and perceptual contrast, contributing to enhanced visual quality and improved target interpretability.
(a) Visible
(b) Infrared
(c) FusionGIAN
(d) NestFFuse
(e) SwinFusion
(i) TarDAL
(g) SegMiFF
(h) SOSMasFFuse
(i) LRRNet
i STFNet
(k) CrossFuse
(i) DDFM
(mn) DiIT-II'
Text Rexl-DiFuse
MMAE
(1) LFDT--Fusin
GIFNet
Ours
Figure Visualization results of several methods on TNO dataset soldiers_witd jeep scene.
Quantitative Evaluation. As shown Table 3 the proposed method consistently outperforms all competing approaches across most metrics. In particular, notable improvements are observed the no-reference metrics, where several indicators exhibit substantial gains- example, AG and DF improve by over 40% relative to the second-best results. The performance advantages demonstrated on the MSRS dataset are well preserved in the TNO dataset, highlighting the model's strong generalization capability. The observed robustness is attributed to the introduction of physics- guided sampling, which enhances the model's ability to capture the intrinsic structure multimodal data. As result, the fusion process becomes more stable and effective under varying scene conditions.
C.3 Comparison FMB dataset
Qualitative Evaluation. To further evaluate the adaptability of HCLFuse to complex and adverse environments, comparative experiments conducted the FMB dataset, which includes diverse weather conditions. The corresponding visual results are illustrated in Fig. g and Fig. 9 Fig. B shows a foggy daytime scene where the fusion objective lies distinguishing salient targets from atmospheric interference. In this scenario, methods such as SegMiF, STFNet, CrossFuse, Diff-IF, Text-DiFuse, MMAE, and LFDT-Fusion fail to preserve the semantic integrity of the pedestrian target within the red box. Although other methods succeed in retaining this target, they struggle to reconstruct background structures, such as the high-rise building. Notably, FusionGAN discards almost all fog-related information, reflecting biased fusion strategy favoring a single modality. In contrast, HCLFFuse is capable simultaneously preserving fog boundaries and target saliency while enhancing background texture fidelity. As result, the output achieves natural and balanced visual appearance, Fig. 9 which presents nighttime scenario, HCLFuse continues to emphasize global clarity and local saliency. Compared to other methods, it delivers comprehensive and
17

---

EXPE NERD
CRPL
NERO
(e) Visible
(b) Infrared
(c) FusionGAN
(d) NustFuse
[el SwinFusion
NERO
NERO
NERO
i TarDAL
(g) SegMiF
SOSMaskFuse
( LRRNet
i STFNet
NEAD
NERD
NEAD
NEAD
NERD
(g) CrosFuuee
( DDFM
(m) DHT-
(a)
NERO
NERO
NERO
(p) Text-Diruse
(q) MMAE
(1) LFDi-Fusion
GlINNet
Ours
Figure 7: Visualization results of several methods on TNO datasct 042 scene.
Table 3: The quantitative metrics of various algorithms in TNO dataset. Bold indicates the best result. underline indicates the second- -bett result.
Method
SDf
AGT
CCt
SCDt
ENT
SFt
Nabf.,
DFt
QSFt
VIFt
PIQEJ
BRI.
FusionGAN
30.663
2.4211
0.4404
1.3793
6,5580
6,2753
0.0816
3.2441
-0.4550
0.4220
23.094
27.802
NestFuse
41.875
3.8485
0.4773
1.6899
7,0465
10.047
0.0328
4.9654
-0.1315
0.8651
22.776
24.693
SwinFusion
39.447
4.2113
0.4744
1.7130
6,8909
10.722
0.0358
5,4839
-0.1168
0.7503
20.655
24.113
Tar
40.141
3.8912
0.4388
1.5842
6.8079
10.621
0.0350
5.0487
-0.0922
0.6006
21.454
24.665
SegMiF
47.609
4.2884
0.4657
1.6577
6,9097
10.721
0.0322
5.1762
-0.0382
0.7028
23.350
25.405
SOSMaskFuse
44.896
3.8377
0.4264
1.5129
7.0393
10.161
0.0658
5.1514
-0.1355
0.8765
21.640
25.888
LRRNet
40.879
3,7690
0.4461
1,5264
6.9881
9.5219
0.0557
5.0105
-0.1674
0,5612
16.415
29.521
STFNet
37,997
2,8956
0.4467
1,5583
6.8148
6.9920
0.0497
3.3568
-0.4066
0.7205
35,979
37.469
CrossFuse
39.674
3.7431
0.4015
1,3436
6.9075
9.9126
0.0731
5.1490
-0.1427
0.7365
20.297
26.882
DDFM
34.295
3.3802
0.5307
1.7770
6.8496
8.5554
0.0443
4.3377
-0.2628
0.6409
19.561
28.662
Diff-IF
39.245
4,2131
0.4468
1.5627
6.8949
11.344
0.0260
5.5671
-0.0735
0.8433
20.635
22.832
Text-IF
46.866
4.6621
0.4614
1.6856
7.1878
11.752
0.0187
5.6968
-0.0179
0.8110
27.487
32.399
CCF
36.888
2,8500
0.5227
1.7999
6.8925
7.3515
0.0512
3.4028
-0.3740
0.5503
31.043
36.625
Text-DiFuse
51.276
3.0653
0.4516
1.6108
7.1521
8.0038
0.0602
3.4862
-0.3407
0.4865
41.602
38.513
MMAE
39.987
3.5560
0.4292
1.5075
6.7764
10.151
0.0330
4.6762
-0.1666
0.8229
25.222
26.
LFDTT-Fusion
40.100
4.1246
0.4483
1.5865
6.9395
10.892
0.0307
5.2708
-0.1078
0.8774
23.424
25.183
GIFNet
40.406
4,9954
0.4966
180110
6.9213
13.358
0.0255
6.0598
0.0817
0.5045
22.094
34.594
Ours
47.726
7.2112
0.4838
1.7673
7.0975
17.625
0.0051
9.1041
0.5264
0.6137
25.710
29.259
perceptually coherent fusion result. This performance is attributed to the cognitive-guides fusion mechanism, where human perception principles are incorporated to enhance the model's ability to perceive, filter, and generate modality-speciffe information, resulting in high-quality generative fusion outputs.
Quantitative Evaluation. The quantitative results on the FMB dataset are reported in Table 4 It can be observed that the performance advantages previously demonstrated on the TNO and MSRS datasets are consistently maintained. Notably, each evaluation metric exhibits considerable relative improvement over the second-best methods. Since the task of image fusion demands both high-fidelity generation and semantic consistency with the source modalities, the fusion results are expected to
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(a) Visible
(b) Infrared
(c) RusionGAN
(d) NestFuse
(c) SwinFusion
(1) TarDAL
(g) SegMiFF
(h) SOSMasFFuse
(i) LRRNet
i STENet
(k) CrossFuse
(i DDFM
(m) DiII-IF
(n) Text IF
(o] CCF
(p) Texi-DiFuse
(q) MMAE
( LFDT--uusion
(s) GlFNeet
(3 Ours
Figure 8: Visualization results of several methods FMB dataset 00005 scene.
preserve the intrinsic characteristics of input images while enhancing perceptual quality. As evidenced across all experimental settings, HCLFuse effectively fulfills these dual objectives, demonstrating superior robustness and consistent performance gains across diverse conditions.
C.4 Comparison on MFNet dataset
Qualitative Evaluation. To further investigate the robustness of HCLFuse, additional experiments are conducted on the MFNet dataset, which includes both daytime and nighttime scenarios. As illustrated in Fig. and Fig. T1] most existing methods struggle clearly reveal fine-grained details such as the bicycles the background, often producing blurred and noisy regions. In contrast, HCLFuse achieves cleaner visual outputs through its pre-fusion information filtering mechanism, effectively suppressing irrelevant artifacts. Moreover, the diffusion-basee generative process enhances the overall image quality and enriches structural and semantic information, yielding visually coherent and detailed fusion results.
Quantitative Evaluation. The quantitative results the MFNet dataset summarized in Table Consistent with previous experiments, HCLFuse maintains stable superiority across all evaluation metrics. Notably, for the five leading indicators, the proposed method consistently ranks first across all four datasets, further highlighting the remarkable generative capability and robustness of HCLFuse in diverse fusion scenarios.
C.5 Comparison MENet dataset
Qualitative Evaluation. To further investigate the robustness HCLFuse, additional experiments are conducted on the MFNet dataset, which includes both daytime and nighttime scenarios. As illustrated in Fig. an] Fig. [T] most existing methods struggle clearly reveal fine-grained details such as the bicycles in the background, often producing blurred and noisy regions. In contrast, HCLFuse achieves cleaner visual outputs through pre-fusion information filtering mechanism, effectively suppressing irrelevant artifacts. Moreover, the diffusion-basee generative process enhances the overall image quality and enriches structural and semantic information, yielding visually coherent and detailed fusion results.
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(a) Visible
(b) Infrared
(c) RusionGAN
(d) NostFuse
(e) SwinFusion
i TarDAL
(g) SegMiFF
(h) SOSMusFFuse
(1) LRRNet
i STFNet
(k) CrussFuse
(i DDFM
(m) DHIT-IF
(n) Text- IF
(0) CCF
(p) Texi-DiFuse
(q) MMAE
(1) LFD'--uusion
(5) GIFNet
(1 Ours
Figure 9: Visualization results of several methods on FMB dataset 00001 scene.
Table 4: The quantitative metrics of various algorithms FMB dataset. Bold indicates the best result. underline indicates the second-best result.
Method
SDT
AGT
CC1
SCDT
ENT
SF1
Nabf]]
DF1
QSF1
VIFt
PIQEL
BRI.
FusionGAN
33.175
2.8539
0.5637
1,2070
6.6987
9.6152
0.0152
3.5124
-0.3202
0.4452
38.193 193
28.462
NestFuse
41.053
4.0541
0.6040
1.5107
6.8918
13.640
0.0070
4.8555
-0.0352
0.8818
33.856
21.748
SwinFusion
40.766
4.6374
0.6194
1.6075
6,8552
15.496
0.0062
5.5574
0.0871
0.8965
30.361
24.851
TarDAL
40.943
3.3670
0,5873
1.5439
6.9166
11.196
0.0061
4.1873
-0.2084
0.6338
28.446
18.729
SegMiF
38.091
3,7627
0.6076
1.5381
6.8625
11.766
0.0101
4,4029
-0.1613
0.6722
37,482
22.024
SOSMaskFuse
36.870
4,3831
0.5464
1.1598
6.7717
14.866
0.0094
5.2575
0.0447
0.9628
32.527
22.598
LRRNet
30.471
3.5878
0.6223
1,3898
6.4809
11.814
0.0112
4.2965
-0.1676
0.6324
33.792
19.414
STFNet
37.496
3.1591
0.5802
1.3915
6.7345
9.6157
0.0100
3.5607
-0.3159
0.6476
51.921
30.633
CrossFuse
29.547
3.7301
0.5424
0,9238
6.4546
12.518
0.0147
4.5228
-0.1135
0.8142
30.349
23.815
DDFM
31,975
2.7999
0.6615
1.6080
6.6920
9.0465
0.0119
3.3765
-0.3608
0.6767
33.487
26.861
DIf
34.229
4.0550
0.5837
1.3669
6.6349
13.870
0.0066
4.9305
-0.0324
0.8717
29.142
20.140
Text-IF
34,552
4,5068
0.6031
1.5119
6.7451
15.050
0.0066
5.3928
0.0546
0.9518
30.216
22.977
CCF
37,310
2.2258
0.6476
1.7469
6.8266
7.4817
0.0135
2,5307
-0.4742
0.5252
49.217
34.470
Text-DiFuse
39,806
3,3414
0.6121
1.5324
6.9141
11.475
0.0121
4.0719
-0.1946
0.5996
26.496
37.312
MMAE
29.307
3.6648
0.5538
1.1878
6.4622
12.443
0,0109
4.3846
-0.1285
0.8526
33.387
22.276
LFDT- Fusion
34.109
4.1004
0.5752
1.3411
6.6373
13.867
0.0082
4.9312
-0.0292
0.7291
33.123
22.705
GIFNet
39.102
5.0481
0.6474
1.7283
6.9011
18.715
0.0043
5.9137
0.2999
0.5931
36.716
25,380
Ours
41.360
6.9814
0.6275
1.6321
7.0580
21.012
0.0018
8.8132
0.4947
0.7629
26.418
33.844
Quantitative Evaluation. The quantitative results on the MFNet dataset are summarized in Table 5 Consistent with previous experiments, HCLFuse maintains stable superiority across all evaluation metrics. Notably, for the five leading indicators, the proposed method consistently ranks first across all four datasets, further highlighting the remarkable generative capability and robustness of HCLFuse in diverse fusion scenarios.
C.6 Segmentation comparison and analysis
The semantic segmentation performance is illustrated in Fig. 12 and Table Fig. 12 presents visual comparisons on both daytime and nighttime scenes from the MSRS dataset. It can be observed that
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(a) Visible
(b) Infrared
(c) RusionGAN
(d) NestFuse
(c) SwinFusion
(I) TarDAL
(g) SegMiF
(h) SOSMusFFuse
( LRRNet
i STFNet
(k) CrussFuse
(i DDFM
(m)) DiIT-IF
(x) Text- IF
(o] CCF
(p) Text-DiFus
(o) MMAE
(1) LFDT-Fusion
(s) GlFNet
(3 Ours
Figure 10: Visualization results of several methods MFNet dataset 01304N scene.
Table 5: The quantitative metrics of various algorithms in MFNet dataset. Bold indicates the best result. underline indicates the second-best result.
Method
SDT
AGT
CC1
SCDT
ENT
SF1
Nabf]]
DF1
QSF1
VIFt
PIQE
BRI
FusionGAN
23.892
2.1156
0.5896
1.2885
6.0584
6.4544
0.0068
2.5495
-0.4253
0.5563
38.692
33.244
NestFuse
39.939
3.6089
0.5677
1.5369
6.6525
11.437
0.0024
4.2067
-0.0540
1.0281
38.603
33.102
SwinFusion
40.076
4.1712
0.5538
1.5156
6.5820
12.867
0.0024
4,8956
0.0816
0.9410
31.849
32.661
TarDAL
36.600
3.3083
0.5681
1.5599
6.6831
10.524
0.0035
4.0647
-0.0536
0.7657
27.878
24.251
SegMiF
36.595
3.0624
0.5601
1,4905
6.3983
9,1332
0.0062
3,4995
-0.2315
0.6699
44.895
34.382
SOSMaskFuse
43.790
3.7247
0.5087
1.3222
6.6309
11.868
0.0042
4.3045
-0.0053
0.9405
40.792
35.119
LRRNet
32.355
2.9572
0.5356
1,3819
6.3787
9.1486
0.0066
3.4833
-0.2174
0.7
30.942
31.166
STFNet
36.427
3.0490
0.5554
1,4466
6,4178
9.0055
0.0033
3.3810
-0,2399
0.8524
51.422
38.718
CrossFuse
34.719
3.2444
0.4836
1.1286
6.5573
9.9844
0.0081
3.8144
-0.1568
0.8669
34.462
33,696
DDFM
30.751
2.7653
0.6082
1.6632
6,4816
8,2793
0.0051
3.2343
-0.2938
0.7792
31.617
32.383
DI
33.823
3,5852
0.5467
1.3797
6.3762
11.468
0.0025
4.2272
-0.0403
0.9191
36,260
33.043
Text-IF
41.337
4.0105
0.5394
1,5982
6.7777
12.275
0.0031
4.6470
0.0379
1.0711
36.734
32.907
CCF
28.369
2.7630
0.6077
1.5403
6,3183
8,9107
0.0041
3.4424
-0.2492
0.7539
20,218
29.497
Text-DiFuse
48,269
3,5280
0.5262
1.4753
7.0612
10.971
0.0050
4.0200
-0.0471
0.7792
36,220
34.261
MMAE
53.914
3.3900
0.3530
0.8330
6.5531
11,549
0.0042
3.8339
0.0505
0.8061
44.603
39.086
LFDT- Fusion
35.264
3.6937
0,5434
1,3966
6.4728
11.402
0.0033
4.2848
-0.0428
0.9713
40.759
34.148
GIFNet
35.754
4.3341
0.5626
1.5210
6.3456
15,246
0.0035
4.9290
0.2699
0.6674
40.658
37.567
Ours
46.386
6.0428
0.5454
1.5466
6.9013
17.424
0.0007
7.2109
0,4782
0.9261
24.135
27.994
FCLFuuse exhibits superior detail preservation and semantic awareness compared to other methods. In addition, the quantitative results in Table show that HCLFuse consistently ranks among the top two across most metrics and achieves the highest mIoU score. These results demonstrate that the fused images generated by HCLFuse are more favorable for downstream semantic segmentation tasks, highlighting its notable advantage in semantic-level fusion quality.
C.7 Additional ablation studies
Effectiveness of physical constraints. To validate the contribution of each physical constraint in HCLFuse, comprehensive ablation study conducted, as summarized in Table 7 Three
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(a) Visible
(b) Infrared
(c) RusionGAN
(d) NostFuse
(c) SwinFusion
(1) TarDAL
(g) SegMiF
(h) SOSMukFuuse
(i LRRNet
i STENet
(k) CrussFFuse
(i DDFM
(mn) DHIT-IF
(x) Text IF
(a] CCF
(p) Text-DiFuse
(q) MMAE
(1) LFDD--Fuion
(6) GllNeet
(1 Ours
Figure 11: Visualization results of several methods MFNet dataset 01493D scene.
Table 6: The quantitative metries of various algorithms in semantic segmentation. Bold indicates the best result. underline indicates the second-best result.
Method
unlabelled
car
person
bike
curve
cur stop
guardrail
color cone
bump
mIOU
Visible
98.97
93.86
75.50
85.48
76.63
85.46
91.56
77.04
88.82
85,92
Infrared
98.74
92.14
79.14
81.91
70.24
72.55
54.83
70.34
83.16
78.12
FusionGAN
99.05
93.71
81,89
85.60
77.54
83.25
89.74
75.76
86.34
NestFuse
99.11
94.13
82.78
86.08
78.04
85.68
92.62
76.77
89.53
87.19
SwinFusion
99.10
94.09
82.87
86. 16
77.71
85.90 $5.94
90.41
76.58
89.45
86.92
TarDAL
99.10
94.08
82.53
86.78
76.98
85.37
91.47
77.44
89.92
87.07
SegMiF
99.10
94.12
82.69
86.41
77.89
84,97
90.13
78.04
89.44
86,98
SOSMaskFuse
99.24
95.02
85.02
88.72
82.43
87.
92.68
78.47
90.01
88.82
LRRNet
99.23
94.93
84.18
82.07
88.56
92.04
80.13
90.29
STRNet
99.23
94.99
84.83
88.83
82.21
87.47
92.51
80.49
90.64
89.02
CrossFuse
99.24
95.06
84.66
88.97
82.02
88.52
92.77
80.27
89.68
89.02
DDFM
99.22
95.05
84.68
88.48
81,95
87.39
92.06
78.27
89.72
88.54
Dift-IF
99.27
95.08
85,38
89.42
83,32
88.51
92.91
$1.07
91.19
89.57
Text-IF
99.27
95.15
85.17
83.38
88.57
91.81
80.61
91.87
89.45
CCF
99.24
95.00
85.18
88. 18
83.07
87.58
92.26
80.40
91.12
89.11
Text-DiFuse
99.27
95.07
84.6
89.24
84.18
88.52
93.08
80.87
91.55
89.60
MMAE
99.25
88.43
84.42
87.84
92.54
80.15
90.40
89.22
LFDY--Fusin
99,28
95.19
85.17
89.02
83.69
88.61
92.57
80,99
91.24
89.53
GIFNet
99.25
95.01
84.74
88.71
83.31
88.14
92.98
80.13
90.50
89.20
Ours
99.28
95.17
89.11
84.60
88.87
92.98
80.70
91.40
89.72
constraint terms were examined individually and jointly, including the heat conduction constraint (Phea), the structure preservation constraint (Pam)) and the physical consistency constraint (#con). Introducing only P leads to noticeable improvements in several perceptual indicators (e.g.. AG, EN, and SF) and achieves the lowest Nabf and PIQE scores, indicating more stable and perceptually faithful generation. When &mm is further incorporated, structural similarity metrics such as SCD are further enhanced, demonstrating that the structure constraint helps maintain edge integrity and sharpness. Finally, including the physical consistency term & eoon yields the best overall performance
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Backgroud
Carstop
Core
Hump
Visdde Snfured
Yadli Iafradd
Crussuus
DOPM
D4S-I1
Tou-I7
CCJ
Teu RiPM
Label
GIPNA
Ten.BPue AMMA LEDT-Fuake
GEPN
(a) 00131D Scene
(b) 01262N Scene
Figure 12: Segmentation visualization results of several methods based on the MSRS dataset.
across almost all metrics, with remarkable gains in SD, AG, EN, SF, and VIF. These results reveal the complementary and synergistic roles of the three physical constraints, collectively contributing enhanced fidelity and stability of the generative process. Overall, the combination of all three physical terms produces the most balanced and high-qualit fusion results, confirming that comprehensive physical modeling is essential for achieving optimal fusion performance.
Table 7: Ablation study on the effectiveness of the designed physical constraints. Bold indicates the best result.
Pheat
Para
Pcan
SDf
AG
OC
SCD1
ENT
SFt
Nabf]
DF
QSP
VIFt
PIQEL
BRIJ
W/O Phest W/O 4
43.11
6.615
0.503
1.899
6.929
15.95
0.004
8.126
0.384
0.520
20.37
37.25
x
x
42.04
6.872
0.504
1.808
7.044
16.56
0.002
8.595
0.448
0.516
18.36
35.05
W/O 4con
x
41.25
6.705
7.025
16.51
0.505
21.21
35.35
Ours
V
47.73
7.211
0.484
1.767
7.098
17.63
0.005
9.104
0.526
0.614
25.71
29.26
Effectiveness of mask components. To further investigate the effectiveness of the mask mechanisms in HCLFuse, we conducted a detailed ablation study covering all three types masks involved in the framework. Two ablation settings were designed to assess their individual and combined contributions, as summarized in Table W/O M, indicates that the semantic mask Mg is removed from the latent representation, which leads to clear performance degradation across multiple perceptual and structural metrics. The results confirm that M, plays an essential role in filtering informative latent variables and reducing redundancy within the bottleneck representation. In another setting, the heat and structure masks (Masat and Msnu) are replaced with all-one masks, effectively removing their spatial selectivity. Performance decreases consistently across key indicators related to detail and structure preservation (e.g., AG, SE, and QSF), indicating that omitting spatial mask guidance weakens both visual quality and perceptual fidelity. The quantitative results demonstrate that each mask plays an indispensable role within its respective mechanism: M, in the bottleneck pathway, and Maea and Msm physically guided image generation. Collectively, these results highlight the necessity of multi-level mask modulation for achieving stable and high-quality fusion performance.
Table 8: Ablation study on the effectiveness of the mask components. Bold indicates the best result.
SDf
AGt
CCt
SCDt
ENt
SFr
Nab[]
DFt
QSFt
VIFt
PIQE,
BRI.
W/O M,
43.98
7.140
0.503
1.752
7.119
16.89
0.002
8.657
0.469
0.568
25.61
32.86
W/O Moea & Maru
39.16
6.821
0.507
1.749
6.991
15.74
0.003
7.974
0.369
0.517
19.12
39.70
Ours
47.73
7.211
0.484
1.767
7.098
17.63
0.005
9.104
0.526
0.614
25.71
29.26
C.8
Broader impacts
Positive Impacts. The proposed method enhances image fusion quality and robustness in degraded scenarios, which can benefit applications such as autonomous driving, medical imaging, and disaster response by improving reliability and safety. Negative Impacts. Advanced fusion capabilities may
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raise concerns about misuse in surveillance or military contexts, Additionally, the method involves computationally intensive models and assumes well-a well-aligned inputs, which may limit accessibility and generalizability.
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The central claim of this paper is address the limitations of generative infrared and visible image fusion by enhancing the diffusion model's understanding of the intrinsic nature of data through the incorporation human cognitive principles. These claims correspond to our contributions and are verified in the methodological and experiment sections.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or
NA answer to this question will not perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
It is fine to include aspirational goals as motivation as long as is clear that these goals attained by the paper.
2. Limitations
Question:
Does the paper discuss the limitations the work performed by the authors?
Answer: [Yes]
Justification: We discussed the limitations of this work in the conclusion. Specifically, its reliance on well-all well-aligned infrared and visible image pairs, as well as the computational overhead introduced by the diffusion process, may constrain its practicality in real-time applications or under resource-limited conditions. Guidelines:
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Reviews:
[{"review_id": "JoRRqju3KZ", "reviewer": "Reviewer_pn8n", "summary": "This paper proposes a novel infrared and visible image fusion method, HCLFuse, aiming to address the limitations of existing generative fusion techniques by incorporating \"human cognitive laws\". The paper designs a method that integrates a variational bottleneck encoder and a physics-guided diffusion model. It demonstrates excellent quantitative and qualitative performance on multiple benchmark datasets, with notable improvements in downstream semantic segmentation tasks. The writing is clear, the experimental section is well-organized, and the ablation studies are fairly comprehensive.", "questions": "Please refer to the Weaknesses.", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. The paper clearly outlines the challenges of image fusion, the limitations of existing methods, and its proposed solutions. The presented framework structure is also intuitive and easy to follow.\n\n2. The paper presents SOTA performance on several infrared and visible image fusion datasets (MSRS, TNO, FMB). Particularly in qualitative results, HCLFuse demonstrates impressive capabilities in detail preservation, structural consistency, and cross-modal information integration.\n\nWeaknesses:\n\n1. The paper elevates mathematical tools like IB and OT to the status of \"human cognitive laws\". Applying them to image fusion, even if their functionality \"resembles\" certain aspects of human cognition (e.g., information filtering, modality alignment), does not directly equate to them being actual manifestations of \"human cognitive laws\" .\n\n2. The \"physical law\" constraints claimed in the paper (Heat Conduction, Structure Preservation, Physical Consistency), in the technical implementation, appear to be common image processing priors or regularization terms. For instance, using the Laplacian operator to promote smoothness is essentially a high-pass filtering or edge detection operation, commonly used in image denoising or enhancement.\n\n3. Despite the excellent model performance, the core components (VBE as a variant of VAE, introducing prior knowledge into diffusion models) are incremental contributions.", "was_revised": true, "final_justification": "The author addressed all of my concerns satisfactorily. I suggest accepting this paper.", "rebuttal": "We sincerely thank you for your insightful and constructive comments regarding the core contributions and scientific novelty of our work. Your thoughtful observations prompted us to more clearly articulate the essence of our contribution. We would like to clarify that the central innovation of this work does not lie in simply applying existing mathematical tools, but in redefining unsupervised image fusion under limited-data settings as a cognition-inspired generative problem. To this end, we present a coherent modeling strategy spanning theoretical motivation, architectural design, and mathematically grounded implementation. This reflects our shift from traditional heuristic fusion pipelines to an interpretable and principled generative approach guided by cognitive processes. Specifically, our innovations span three levels:\n\n*A cognition-inspired image fusion framework is proposed by reformulating the fusion task as a generative problem, aiming to obtain high-quality fused images beyond the limitations of individual modalities.\n\n*An unsupervised information mapping and quantization theory is proposed to characterize the trade-off between multi-source information compression and discriminability, based on which a multi-scale variational bottleneck encoder is designed to extract task-relevant low-level feature representations.\n\n*A dynamically evolving physics-guided mechanism is incorporated into the diffusion process, where temporally progressive physical priors enhance structural perception and generalization, effectively reducing dependence on large-scale training data.\n\nTo address your concerns in detail, we respond to your comments point by point below.\n\n**Response to Weakness 1:** \n\nRegarding your concern about the use of the phrase “human cognitive laws” , we fully acknowledge that this wording may have unintentionally overstated our intention. We have revised the manuscript to clarify that our framework is guided by **cognition-inspired modeling principles**, rather than literal human cognitive laws. Specifically, we are inspired by how humans process multi-source information: filtering redundant content, attending to salient cues, and forming abstract internal representations. The Information Bottleneck (IB) principle models the selective compression process inherent in such cognitive abstraction, while Optimal Transport (OT) provides a tractable and unsupervised alternative objective that enables alignment and regularization of cross-modal information. This theoretical foundation is detailed in Theorem 1. Together, these tools are not themselves “cognitive laws,” but are used to instantiate a formal, interpretable, and optimizable framework that reflects cognition-like information processing. We hope this can clarifies our conceptual positioning.\n\n**Response to Weakness 2:**\n\nThank you for raising this important point. We fully agree that individual components such as Laplacian smoothing have been used as traditional image priors. However, the novelty of our work does not lie in each constraint’s isolated form, but in how they are dynamically and sequentially embedded into the generative diffusion process through a mechanism we term Time-varying Physical Guidance (TPG).\nThis mechanism introduces three physically-inspired constraints—heat conduction ($\\Phi_{\\text{heat}}$), structure preservation ($\\Phi_{\\text{stru}}$), and cross-modal consistency ($\\Phi_{\\text{con}}$)—that are:\n\nApplied in sequence to the predicted sample at each reverse diffusion timestep;\n\nModulated by a decaying coefficient $\\lambda(t)$ that mimics coarse-to-fine reasoning;\n\nDesigned to stabilize generation and guide physical structure, not merely smooth pixels.\n\nThis guidance process mimics the cognitive behavior of humans resolving ambiguous information by applying strong structural priors early and gradually relaxing them as certainty increases. \n\n**Response to Weakness 3:**\n\nWhile our Variational Bottleneck Encoder (VBE) adopts a variational formulation, it is not a simple extension of the conventional VAE. The key distinction lies in both its theoretical foundation and functional role. VBE is directly motivated by our theoretical result in Theorem 1, where we reformulate unsupervised image fusion as an information quantization problem, leading to a principled, task-aware latent abstraction objective that fundamentally differs from the reconstruction-driven design of standard VAEs.\n\nMore importantly, VBE serves as a functional bridge between mutual information optimization and the generative process. It provides the diffusion model with a structurally coherent and task-relevant latent space, effectively relieving the burden of handling complex cross-modal alignment and selection. This allows the diffusion model to focus on modeling high-quality fused outputs guided by the compressed priors. Notably, most existing diffusion-based methods focus solely on improving the architecture itself, while overlooking the task relevance and informational clarity of the latent inputs. Our approach addresses this upstream bottleneck optimizing the content of the latent space, thereby enhancing both the generative ability and fusion quality.\n\n**Supplementary Experiments:**\n\nTo further support your concerns on originality and effectiveness, we have conducted comprehensive additional experiments:\n\n(1) An ablation analysis of both the internal structure and overall design of VBE demonstrates its critical role in compact encoding;\n\n(2)A fine-grained ablation study on each physical constraint confirms their individual benefits and complementary synergy;\n\n(3)A hyperparameter study over $\\beta$, $\\lambda_{\\text{heat/stru/con}}$, and decay factor $\\gamma$ validates the robustness of our design choices;\n\n(4) Comparative evaluations have been expanded by incorporating additional top-tier conference methods, resulting in a total of seventeen compared approaches (e.g., TarDAL [CVPR 2022], DDFM [ICCV 2023], Text-DiFuse [NeurIPS 2024], Text-IF [CVPR 2024], GIFNet [CVPR 2025]), and are conducted across twelve quality metrics, including spatial detail, structural preservation, and perceptual quality;\n\n(5) The MFNet dataset has been additionally introduced, and the experimental results further verify the generalization ability of the proposed method under low-quality scenarios.\n\nThank you once again for your thoughtful comments, which have significantly improved our presentation and helped sharpen the contribution of our work. We hope the revised version addresses your concerns and clarifies the novelty and value of our approach."}, {"review_id": "J9ZaAM142o", "reviewer": "Reviewer_bhfu", "summary": "This paper proposes a generative infrared and visible image fusion method based on human cognitive laws, named HCLFuse. By integrating a variational bottleneck encoder and a physics-guided diffusion model, it enhances the structural consistency and detail quality of fused images.", "questions": "Here are several problems and suggestion for authors to improve their paper.\n\n(1) In Eq.6, how are the mask weights $M_s$ generated? It is recommended to provide a sufficient explanation.\n\n(2) The Method section lacks an overview of the entire HCLFuse process. It is recommended that the authors add a textual description or pseudocode in the main text or appendix to outline the complete workflow from inputs (X, Y) to output Z.\n\n(3) VIF (Visual Information Fidelity) demonstrates greater advantages in perceptual quality assessment and is more widely applied. It is recommended to replace the QSF metric with VIF, which is also more relevant to the \"Human Cognitive Laws\" mentioned in the paper's title.\n\n(4) The ablation experiments are insufficiently comprehensive, as the authors do not validate the role of the heat conduction constraint. It is recommended to add relevant experiments to address this gap.\n\n(5) In the ablation experiments subsection, what is DDIM? What is \"deterministic sampling\"? It is recommended to provide clear explanations.", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "This paper innovatively introduces human cognitive laws into generative image fusion, proposing a novel HCLFuse framework. Its unique innovations lie in the design of a multi-scale variational bottleneck encoder and a time-varying physics-guided mechanism, which distinguish it from both traditional methods and existing generative approaches.\n\nHCLFuse demonstrates high fusion quality: it achieves optimal performance across multiple quantitative metrics (e.g., AG, SF, DF, QSF) on datasets such as MSRS, TNO, and FMB. In qualitative evaluations, it effectively preserves complementary features, exhibits clear details, and ensures strong structural consistency, outperforming various comparative methods.\n\nHowever, the theoretical framework is overly abstruse, imposing certain requirements on readers' prior knowledge. This may hinder the accessibility of the research findings to a broader audience.", "was_revised": true, "final_justification": "The authors have addressed my concerns and revised the paper as suggested, improving its quality. It is acceptable.", "rebuttal": "We sincerely thank you for your constructive feedback and recognition of the contributions of our work. Below, we address each of the reviewer’s concerns in detail. \n\n**Response to Weakness:** \n\nTo address the concern that parts of the theoretical presentation may hinder accessibility for a broader audience, \n\n(1) We have carefully revised the manuscript based on your comments, improving unclear expressions and enhancing clarity throughout;\n\n(2) We have added more references to support the foundational theories involved; \n\n(3) We will release the complete code in due course to promote transparency and reproducibility.\n\n**Response to Question 1:** \n\nThank you for the comment regarding the mask weights $M_s$ in Eq.~(6), which represent a critical component of the proposed design. At each encoder scale, a structurally defined learnable parameter $w_s \\in \\mathbb{R}^{1 \\times C \\times 1 \\times 1}$, where $C$ denotes the number of feature channels, is introduced. The parameter is initialized from a standard Gaussian distribution using “torch.randn()\" and registered as an \"nn.Parameter\" in the model. During forward propagation, $M_s = \\mathrm{sigmoid}(w_s)$, forming a differentiable soft mask applied to the feature map. Throughout training, $w_s$ is optimized under the guidance of the information bottleneck loss $\\mathcal{L}_{\\text{VBE}}$, functioning as an auxiliary modulation mechanism that enhances the selective suppression of redundant information within the latent representation. \n\nTo address your concern, we conduct a dedicated ablation study to examine the individual and joint contributions of the mask mechanism and the information bottleneck loss. The results clearly show that removing either component degrades performance, while the best results are consistently achieved only when both are jointly applied. This highlights the necessity of their integration in realizing effective latent compression.\n\n**Response to Question 2:** \n\nThank you for this helpful suggestion. We have added an overview of the entire HCLFuse process at the beginning of the Method section. In addition, we have included a pseudocode description of the complete workflow in the appendix to further enhance reproducibility and clarity. To facilitate your review, we present below the textual description as it appears in the Method section:\n\n\"In HCLFuse, let the infrared image domain be denoted as $X$ and the visible image domain as $Y$, with joint observations satisfying $(x, y) \\sim p_{x,y}$. We construct a fusion mapping $F(X, Y) \\rightarrow Z$ to generate the fused latent representation $z = F(x, y) \\in Z$, where $Z \\subseteq \\mathbb{R}^d$ denotes the fusion space. Ideally, the fused representation should effectively preserve the complementary information from both modalities while suppressing redundant noise. To achieve this, HCLFuse first employs a multi-scale, mask-regulated variational bottleneck encoder to compress and model the latent representation $z$. Before encoding, an optimal transport-based mapping is applied to improve the optimization lower bound, encouraging $z$ to contain modality-discriminative content in the unsupervised setting. Subsequently, $z$ is refined through a reverse-time diffusion generation process, where physically guided constraints are dynamically injected at each denoising timestep to guide the evolution of latent features. Finally, the optimized latent representation is decoded to produce the fused image.\"\n\n**Response to Question 3:** \n\nRegarding your suggestion on the use of VIF, we agree that VIF is a widely adopted full-reference metric for perceptual quality assessment and aligns well with the \"Human Cognitive Laws\" emphasized in our title. In response, we have included additional experiments and reported VIF results for completeness. However, VIF is a full-reference metric that relies on source images, which conflicts with the goal of generative fusion. Our method is designed to synthesize richer and more complete information by integrating both modalities, and as illustrated in Figures 3, 4, and 6–9 in the paper, the fused outputs often surpass the original inputs in clarity, detail, and semantic structure. As a result, using source images as references tends to underestimate the perceptual quality of the generated fusion results.\n\nTo address this, we have further introduced two no-reference perceptual metrics—PIQE [IEEE NCC, 2015: 1–6.] and BRISQUE [IEEE TIP, 2012: 4695–4708.]—which are well-suited for evaluating visual quality without requiring reference images. They capture distortions and unnatural statistics in a way that correlates with human perception. As shown in the table below, although our method achieves a moderate score on the VIF metric, it demonstrates superior performance on both the PIQE and BRISQUE metrics, highlighting its perceptual quality in image generation.\n\nThe best results are highlighted in bold, while the second-best results are marked with a † symbol.\n\n|Method|MSRS|dataset||FMB|dataset||TNO|dataset||MFNet|dataset||  \n|--------------|------------------|-------|--------|------------------|-------|--------|------------------|-------|--------|------------------|-------|--------|  \n||VIF↑|PIQE↓|BRISQUE↓|VIF↑|PIQE↓|BRISQUE↓|VIF↑|PIQE↓|BRISQUE↓|VIF↑|PIQE↓|BRISQUE↓|  \n|FusionGAN|0.4627|44.12|35.54|0.4452|38.19|28.46|0.4220|23.09|27.80|0.5563|38.69|33.24|  \n|NestFuse|0.9099|41.86|37.51|0.8818|33.86|21.75|0.8651|22.78|24.69|**1.0281**|38.60|33.10|  \n|SwinFusion|0.9119|38.14|37.55|0.8965†|30.36|24.85|0.7503|20.66|24.11†|0.9410|31.85|32.66|  \n|SegMiF|0.6239|42.08|34.44|0.6722|37.48|22.02|0.7028|23.35|25.41|0.6699|44.90|34.38|  \n|SOSMaskFuse|0.8585|44.77|40.06|**0.9628**|32.53|22.60|0.8765†|21.64|25.89|0.9405|40.79|35.12|  \n|LRRNet|0.5680|29.96|30.82|0.6324|33.79|**19.41**|0.5612|**16.42**|29.52|0.7167|30.94†|31.17|  \n|STFNet|0.8463|54.53|43.03|0.6476|51.92|30.63|0.7205|35.98|37.47|0.8524|51.42|38.72|  \n|CrossFuse|0.8374|33.08|33.59|0.8142|30.35|23.82|0.7365|20.30†|26.88|0.8669|34.46|33.70|  \n|Diff-IF|**1.0417**|32.73|31.51|0.8717|29.14†|20.14†|0.8433|20.63|**22.83**|0.9191|36.26|33.04|  \n|CCF|0.6847|**17.34**|26.38†|0.5252|49.22|34.47|0.5503|31.04|36.63|0.7539|20.22|29.50†|  \n|MMAE|0.8090|33.18|31.25|0.8526|33.39|22.28|0.8229|25.22|26.16|0.8061|44.60|39.09|  \n|LFDT-Fusion|1.0296†|38.86|32.37|0.7291|33.12|22.71|**0.8774**|23.42|25.18|0.9713†|40.76|34.15|  \n|**Ours**|0.8540|25.19†|**26.22**|0.7629|**26.42**|33.84|0.6137|25.71|29.26|0.9261|**24.13**|**27.99**|\n\n**Response to Question 4:** \n\nWe sincerely thank you for pointing out the lack of explicit validation for the heat conduction constraint. To address this concern, we have conducted a comprehensive ablation study targeting each physical constraint. The results are summarized in the updated Table:\n\nThe best results are highlighted in bold.\n||$\\Phi_\\text{heat}$|$\\Phi_\\text{stru}$|$\\Phi_\\text{con}$|SD↑|AG↑|CC↑|SCD↑|EN↑|SF↑|Nabf↓|DF↑|QSF↑|VIF↑|PIQE↓|BRISQUE↓|\n|----------------------------|------|------|------|---------|---------|---------|---------|---------|---------|---------|---------|---------|---------|---------|-----------|\n|A|✗|✗|✗|43.1107|6.6147|0.5028|1.8086|6.9285|15.9481|0.0040|8.1256|0.3838|0.5196|20.3652|37.2477|\n| B | ✓    | ✗    | ✗    | 42.0447 | 6.8718  | 0.5035  | 1.8076  | 7.0436  | 16.5597 | **0.0029**  | 8.5954  | 0.4480  | 0.5164  | **18.3564** | 35.0540   |\n| C | ✓    | ✓    | ✗    | 41.2477 | 6.7049  | **0.5080**  | **1.8183**  | 7.0248  | 16.5085 | 0.0034  | 8.6943 | 0.4319  | 0.5054  | 21.2138 | 35.3540   |\n| Ours | ✓    | ✓    | ✓    | **47.7260** | **7.2112**  | 0.4838  | 1.7673  | **7.0975**  | **17.6254** | 0.0051  | **9.1041**  | **0.5264**  | **0.6137**  | 25.7097 | **29.2594** |\n\nExperimental Analysis:\n\nFrom A → B, introducing only the heat conduction constraint $\\Phi_{\\text{heat}}$ already improves several perceptual indicators (e.g., AG, EN, SF), and achieves the lowest Nabf and PIQE.\n\nFrom B → C, adding the structure preservation constraint $\\Phi_{\\text{stru}}$ further enhances structural similarity (e.g.,SCD), showing that $\\Phi_{\\text{stru}}$ helps maintain sharpness and edge consistency.\n\nFrom C → Ours, the inclusion of the physical consistency constraint $\\Phi_{\\text{con}}$ results in the best overall performance across almost all metrics, with notable gains in SD, AG, EN, SF, and VIF. This underlines the complementary and synergistic role of the three constraints.\n\nThese results collectively validate the effectiveness of each individual physical constraint, and further demonstrate that only the full combination of all three can maximize overall fusion performance.\n\n**Response to Question 5:** \n\nWe sincerely thank you for pointing out the lack of clarity regarding DDIM. This is indeed an important component that was not sufficiently explained in the original version. DDIM (Denoising Diffusion Implicit Models) [Song et al., ICLR 2021] is a deterministic variant of the standard diffusion model DDPM(Denoising Diffusion Probabilistic Models) [NeurIPS, 2020: 6841–6853]. In our framework, DDIM serves as the core generative module. In the ablation setting \"W/O DDIM\", we remove the entire diffusion process and obtain the fused image directly through an encoder-decoder structure. As shown in Table 2 and Figure 5 in the paper, this leads to a loss of fine details and noticeable drops in several metrics, confirming that the generative process provides essential complementary information for high-quality fusion. Additional explanations and the relevant citation have been added in the revised manuscript for clarity."}, {"review_id": "YoVysNtOUG", "reviewer": "Reviewer_cS6j", "summary": "This paper proposes an infrared and visible image fusion framework based on generative models. This framework designs a multi-scale variational bottleneck encoder to extract structured low-level features and a physics-aware diffusion process to guide this extraction process. They provide extensive experiments to verify the effectiveness of this work across multiple fusion scenarios and in downstream semantic segmentation tasks.", "questions": "a)\tTo my understanding, MSRS serves as an enhanced version of the MFNet [IEEE IROS, 2017: 5108-5115.] dataset. Have the authors considered evaluating the proposed algorithm on MFNet to assess its performance on lower-quality data?\nb)\tIn line 134, Clark's viewpoint is mentioned without a corresponding citation.\nc)\tBesides, please address the concerns in the Weakness part.", "limitations": "Yes.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The idea of revisiting generative infrared and visible image fusion from the perspective of human cognitive principles is novel.\n2. The authors present theoretical constructs and theorems to substantiate the proposed method.\n3. The authors conduct extensive evaluations, proving the advanced performance of this work.\n\nWeaknesses：\n1. In line 162, Why are only infrared images transformed? And what are the specific transformations applied? Providing detailed explanations would improve the clarity and readability of the manuscript.\n2. In line 174, $\\M_s$ represents mask weights that determine the importance of each feature. However, it is unclear how these weights are derived. Please provide a detailed explanation.\n3. Also, the process of obtaining $\\M_{heat}$ and $\\M_{stru}$ should be described. Providing further detailed explanations would enhance the understanding of the overall method.\n4. Have the authors evaluated the necessity of the aforementioned mask weights and analyzed their impact on the overall experimental results? It would be helpful if the authors could provide additional explanations to clarify this aspect.\n5. In Table 5, the content in the last column appears to exceed the boundary. The authors may need to check the table formatting to ensure proper alignment.", "was_revised": true, "final_justification": "I am inclined to maintain my rating.", "rebuttal": "We sincerely thank you for the valuable comments and suggestions. We have carefully revised the manuscript according to your feedback. All modifications have been incorporated into the revised version. Below, we respond to each of your concerns in detail.\n\n**Response to Weakness 1:** \n\nIn our framework, we deliberately apply transformation only to the infrared image while keeping the visible image unchanged. This design choice is primarily based on two considerations:\n\n(1) **Modality discrepancy**: Infrared images generally contain less structural detail and more semantic ambiguity compared to visible images due to inherent sensor limitations. Therefore, aligning the infrared modality towards the visible one — which typically exhibits more stable textures and clearer semantic content — helps to reduce cross-modal discrepancies and facilitates the learning of more consistent joint representations.\n\n(2) **Computational efficiency and robustness**: Aligning both modalities simultaneously would require computing and optimizing two separate transport plans, increasing training complexity and introducing potential inconsistencies between mappings. A one-sided transformation from infrared to visible ensures efficiency and stability during optimization.\n\nIn practice, we compute a regularized optimal transport plan using the following formulation:\n$$\n\\mathbf{P}^* = \\arg\\min_{\\mathbf{P} \\in \\mathcal{U}(r, c)} \\sum_{i,j} P_{ij} C_{ij} + \\varepsilon \\sum_{i,j} P_{ij} \\log P_{ij},\n$$\nwhere $\\mathcal{U}(r, c)$ denotes the set of doubly stochastic matrices with row and column marginals $r$ and $c$, $C_{ij}$ is the cost matrix computed by squared Euclidean distances between flattened pixel values of the infrared and visible images, and $\\varepsilon$ is a regularization coefficient.\nAfter obtaining the transport plan $\\mathbf{P}^*$, the infrared image $X \\in \\mathbb{R}^{B \\times C \\times H \\times W}$ is first flattened to $X_{\\text{flat}} \\in \\mathbb{R}^{B \\times N \\times C}$, where $N = H \\times W$, and transformed by:\n\n$$\nX' = \\mathbf{P}^* \\cdot X_{\\text{flat}},\n$$\n\nfollowed by reshaping back to $\\mathbb{R}^{B \\times C \\times H \\times W}$. The transformed infrared image $X'$ and the original visible image $Y$ are then fed into the variational bottleneck encoder for subsequent fusion modeling.\nWe have added this explanation and implementation clarification in the revised manuscript for better clarity.\n\n**Response to Weakness 2:** \n\nThank you for raising the questions regarding the derivation and effectiveness of the mask weights $M_s$ described in line 174. To clarify, the mask weights $M_s$ are computed as $M_s = \\mathrm{sigmoid}(w_s)$, where $w_s \\in \\mathbb{R}^{1 \\times C \\times 1 \\times 1}$ is a learnable parameter introduced at each encoder scale. This parameter is initialized from a standard Gaussian distribution using \"torch.randn()\" and registered as an “nn.Parameter\". The sigmoid activation ensures that the mask values remain within a continuous range of $(0, 1)$, enabling soft, differentiable modulation of feature importance.\n\n**Response to Weakness 3:** \n\nWe sincerely thank you for highlighting the importance of clarifying the construction process of the masks $M_{\\text{heat}}$ and $M_{\\text{stru}}$. These masks are explicit, non-learnable spatial priors designed based on statistically meaningful physical patterns inherent in the input modalities.\n\n*$M_{\\text{heat}}$ is derived from the thermal intensity distribution of the infrared image. Specifically, we apply a $3 \\times 3$ average pooling to obtain a local heat response map, then compare each pixel to the global mean value to construct a binary mask—pixels above the mean are set to 1, others to 0. This explicitly highlights thermally significant regions and models localized heat saliency.\n\n*$M_{\\text{stru}}$ captures structural saliency in the visible image by applying a Laplacian operator to extract high-frequency edge responses (i.e., local second-order derivatives). This structural response map reflects intensity variations along object boundaries. We binarize it via thresholding at the mean, thereby identifying regions with prominent structural features.\n\nBoth masks are dynamically updated at each reverse diffusion timestep, providing a time-varying guidance mechanism that adaptively emphasizes thermal or structural cues depending on the generation stage. This design enhances modality complementarity while reinforcing physical consistency and interpretability throughout the sampling trajectory.\n\n**Response to Weakness 4:** \n\nTo address your concern, we conduct a detailed ablation study covering all three types of masks involved in our framework.\n\nAblation 1 — W/O $M_s$:\n\nWe remove the $M_s$ from the latent representation and observe clear performance degradation across multiple perceptual and structural metrics, confirming that $M_s$ is essential for filtering informative latent variables and reducing redundancy. \n\nAblation 2 — W/O $M_{\\text{heat}}$ & $M_{\\text{stru}}$:\n\nWe replace $M_{\\text{heat}}$ and $M_{\\text{stru}}$ with all-one masks, effectively removing their spatial selectivity. The performance drops consistently across key indicators related to detail and structure preservation (e.g., AG, SF, QSF), indicating that omitting spatial mask guidance weakens both visual quality and perceptual fidelity.\n\nThe results are summarized in the revised ablation table (see Appendix), which is reproduced below for clarity. These findings demonstrate that each mask plays an indispensable role in its corresponding mechanism: $M_s$ in the bottleneck pathway, and $M_{\\text{heat}}$, $M_{\\text{stru}}$ in physically-guided image generation.\n\nThe best results are highlighted in bold.\n\n|| SD↑ | AG↑ | CC↑ | SCD↑ | EN↑ | SF↑ | Nabf↓ | DF↑ | QSF↑ |\n|---------|------|------|------|--------|------|-------|--------|------|-------|\n| W/O $M_s$ | 43.9840 | 7.1404 | 0.5026 | 1.7520| **7.1191** | 16.8872 | **0.0023** | 8.6568 | 0.4692 |\n| W/O $M_\\text{heat}$ \\& $M_\\text{stru}$ | 39.1644 | 6.8207 | **0.5070** | 1.7492 | 6.9906 | 15.7408 | 0.0031 | 7.9742 | 0.3687 |\n| Ours | **47.7260** | **7.2112** | 0.4838 | **1.7673**| 7.0975 | **17.6254** | 0.0051 | **9.1041** | **0.5264** |\n\n**Response to Weakness 5:** \n\nWe have revised the layout of Table 5 to ensure that the last column is properly aligned and fits within the page boundaries. Additionally, we have checked and adjusted the formatting of all other tables to ensure consistent alignment and presentation throughout the manuscript.\n\n**Response to Question a:**\n\nWe sincerely thank you for raising this important point. In response, we have conducted additional experiments on the MFNet dataset. As shown in the results table below, our method retains strong robustness and generalization ability even under degraded sensor conditions.\n\nThe best results are highlighted in bold, while the second-best results are marked with a † symbol.\n\n|Method|SD↑|AG↑|CC↑|SCD↑|EN↑|SF↑|Nabf↓|DF↑|QSF↑|\n|------|----|----|----|-----|----|----|-----|----|-----|\n|FusionGAN|23.8915|2.1156|0.5896†|1.2885|6.0584|6.4544|0.0068|2.5495|-0.4253|\n|NestFuse|39.9392|3.6089|0.5677|1.5369|6.6525†|11.4370|0.0024†|4.2067|-0.0540|\n|SwinFusion|40.0755|4.1712†|0.5538|1.5156|6.5820|12.8671†|0.0024†|4.8956†|0.0816†|\n|SegMiF|36.5946|3.0624|0.5601|1.4905|6.3983|9.1332|0.0062|3.4995|-0.2315|\n|SOSMaskFuse|43.7900|3.7247|0.5087|1.3222|6.6309|11.8681|0.0042|4.3045|-0.0053|\n|LRRNet|32.3548|2.9572|0.5356|1.3819|6.3787|9.1486|0.0066|3.4833|-0.2174|\n|STFNet|36.4268|3.0490|0.5554|1.4466|6.4178|9.0055|0.0033|3.3810|-0.2399|\n|CrossFuse|34.7190|3.2444|0.4836|1.1286|6.5573|9.9844|0.0081|3.8144|-0.1568|\n|Diff-IF|33.8229|3.5852|0.5467|1.3792|6.3762|11.4678|0.0025|4.2272|-0.0403|\n|CCF|28.3691|2.7630|**0.6077**|1.5403†|6.3183|8.9107|0.0041|3.4424|-0.2492|\n|MMAE|**53.9142**|3.3900|0.3530|0.8330|6.5531|11.5492|0.0042|3.8339|0.0505|\n|LFDT-Fusion|35.2642|3.6937|0.5434|1.3966|6.4728|11.4020|0.0033|4.2848|-0.0428|\n|Ours|46.3864†|**6.0428**|0.5454|**1.5466**|**6.9013**|**17.4240**|**0.0007**|**7.2109**|**0.4782**|\n\n\n**Response to Question b:**\n\nWe sincerely apologize for the omission. We have added the citation to Clark's work in the revised manuscript (Line 134):\n\nClark, A. \"Whatever next? Predictive brains, situated agents, and the future of cognitive science.\" Behavioral and Brain Sciences, 2013, 36(3): 181–204.\n\n\n**Response to Question c:**\n\nWe appreciate your advice and have carefully addressed the concerns raised in the Weaknesses section."}, {"review_id": "b5VE8AglHp", "reviewer": "Reviewer_x9j9", "summary": "This paper introduces HCLFuse, a novel generative framework for infrared and visible image fusion, which is conceptually motivated by principles from human cognition. \nIt consists of two stage:\n1. In the first stage, the authors design a Variational Bottleneck Encoder (VBE) to learn a compact yet comprehensive latent representation from the source images. The design and optimization of this encoder are formally grounded in the Information Bottleneck (IB) theory.\n2. In the second stage, a conditional diffusion model synthesizes the final fused image from this latent code. The key innovation in this stage is that the reverse diffusion process is not purely data-driven. Instead, it is guided at each timestep by a set of proposed physics-based constraints, including terms that enforce heat conduction properties and structural preservation.", "questions": "I thank the authors for this novel and inspiring work. To further strengthen the paper and address the potential weaknesses, I hope the authors can provide further elaboration on the following key points. My final evaluation will be heavily influenced by the responses to the **weakness 1** and **weakness 2**.", "limitations": "Yes.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strengths\n\n1. The paper's primary strength is its exceptional novelty. It introduces a highly principled framework for image fusion, conceptually grounded in human cognition and physical laws. This represents a significant and refreshing departure from conventional methods that often rely on data-driven regression with generic losses (e.g., L1 or SSIM), opening a promising new direction for the field.\n2. The paper is exceptionally well-written and logically structured. The authors present a compelling narrative that connects cognitive science, information theory, and physics-based modeling. Crucially, they support their claims with both detailed theoretical derivations (Theorems 1 and 2) and strong empirical evidence from extensive experiments.\n3. The proposed method, HCLFuse, achieves state-of-the-art results. It demonstrates superior performance against a wide range of existing methods across multiple public datasets and evaluation metrics, in terms of both quantitative scores and qualitative visual quality.\n\n## Weaknesses\n\n1. The selection of baseline methods for comparison could be more contemporary and representative of the current state-of-the-art. A significant portion of the compared methods in Table 1 are from journals, which may have longer publication cycles and may not reflect the latest advancements. The comparison would be more compelling if the proposed method were benchmarked against more recent, highly relevant methods from top-tier conferences. For instance, recent works like TarDAL, DDFM, and the emerging diffusion-based or text-guided methods such as SegMIF, Text-IF, or Text-Difuse would serve as more challenging and appropriate baselines to truly validate the claimed state-of-the-art performance.\n2. Insufficient Ablation of Individual Physical Constraints: In Section 3.3, the authors propose three distinct physical constraints: Heat Conduction, Structure Preservation, and Physical Consistency. However, the ablation study in Section 4.6 only evaluates their collective impact (the \"w/o TPG\" variant). This makes it impossible to understand the individual contribution of each constraint. The paper would be much stronger if it included a more fine-grained ablation study to demonstrate which of these physical principles are most critical to the model's success.\n3. Ambiguity in Constraint Application: There is a minor ambiguity in the method's description in Section 3.3. Equation 14 applies the Heat Conduction constraint to the predicted clean data $\\hat{z}_0$. However, the subsequent Equations 15 and 16, for the Structure and Consistency constraints, are formulated with the variable $z$. It should be clarified whether all constraints are applied to $\\hat{z}_0$ or consistency, or if they are indeed applied to different $z_t$ during the whole recovery process.\n4. Lack of Hyperparameter Analysis: The model introduces a significant number of hyperparameters, particularly the initial weights $\\lambda_{i}^{0}$ and the decay factor $\\gamma$ in the Time-varying Physical Guidance (TPG) mechanism. The paper does not provide details on how these were selected or discuss the model's sensitivity to them. A discussion of the tuning strategy and a sensitivity analysis would significantly improve the paper's claims of robustness and its practical value for reproducibility.", "was_revised": true, "final_justification": "I read all responses from the author. My concerns are mostly solved, and now I am learning to accept this paper.", "rebuttal": "We deeply appreciate your thoughtful and detailed review. In response, we have carefully revised the manuscript to address all your comments. A point-by-point response is provided below.\n\n**Response to Weakness 1:** \n\nWe fully agree that comparing against more contemporary and competitive approaches is crucial to demonstrating the effectiveness of our proposed method. We have conducted extensive additional experiments to include the recent state-of-the-art methods mentioned in the review, such as TarDAL, DDFM, SegMIF, Text-IF, CCF, Text-DiFuse, and GIFNet. These methods represent leading advances from top-tier conferences. To present a more comprehensive comparison, we have  included a more challenging dataset, MFNet, to further validate the robustness and generalization ability of our method on low-quality image pairs.\nThe additional experimental results are shown in the table below. To improve clarity, we use bold to indicate the best result and † to mark the second-best. Overall, the proposed method consistently achieves superior performance across most metrics, confirming its robustness and generalization ability even under comparison with the latest and strongest baselines. \n\n（Due to the rebuttal's character limit, we only present the fusion performance here, while the evaluation on the segmentation task is included in the revised manuscript. If needed, we would be happy to provide those results separately.）\n\n**(1)MSRS Dataset**\n| Metric     | TARDAL (CVPR 2022) | Seg_MiF (ICCV 2023) | DDFM (ICCV 2023) | Text-IF (CVPR 2024) | CCF (NeurIPS 2024) | Text-DiFuse (NeurIPS 2024) | GIFNet (CVPR 2025) | Ours |\n|:-----|:---------------|:----------------|:-------------|:----------------|:---------------|:------------------------|:----------------|:----|\n|SD↑|35.4604|40.3512|28.9225|44.5884|28.9463|**54.2426**|32.9010|49.5464†|\n|AG↑|3.1149|3.0449|2.5219|3.8811†|2.8753|3.7000|3.3673|**6.4355**|\n|CC↑|0.6261|0.6130|**0.6585**|0.5982|0.6495†|0.5710|0.6278|0.6186|\n|SCD↑|1.4837|1.5482|1.4493|**1.6976**|1.4087|1.3816|1.4082|1.6575†|\n|EN↑|6.3476|6.3297|6.1748|6.7280|6.1917|**7.1436**|5.9404|6.8704†|\n|SF↑|9.8729|9.4273|7.3879|11.8790|9.0417|11.4080|12.7048†|**17.8985**|\n|Nabf↓|0.0098|0.0177|0.0196|0.0075†|0.0154|0.0125|0.0110|**0.0017**|\n|DF↑|3.8817|3.4624|2.9599|4.5075†|3.6091|4.1898|3.8030|**7.6427**|\n|QSF↑|-0.1419|-0.1968|-0.3581|0.0165|-0.2369|-0.0168|0.0654†|**0.5374**|\n\n**(2)FMB Dataset**\n\n|Metric|TARDAL (CVPR 2022)|Seg_MiF (ICCV 2023)|DDFM (ICCV 2023)|Text-IF (CVPR 2024)|CCF (NeurIPS 2024)|Text-DiFuse (NeurIPS 2024)|GIFNet (CVPR 2025)|Ours|\n|:-----|:----------------|:------------------|:---------------|:------------------|:------------------|:--------------------------|:------------------|:----|\n|SD↑|40.9429†|38.0909|31.9747|34.5523|37.3103|39.8064|39.1017|**41.3603**|\n|AG↑|3.3670|3.7627|2.7999|4.5068|2.2258|3.3414|5.0481†|**6.9814**|\n|CC↑|0.5873|0.6076|**0.6615**|0.6031|0.6476†|0.6121|0.6474|0.6275|\n|SCD↑|1.5439|1.5381|1.6080|1.5119|**1.7469**|1.5324|1.7283†|1.6321|\n|EN↑|6.9166†|6.8625|6.6920|6.7451|6.8266|6.9141|6.9011|**7.0580**|\n|SF↑|11.1957|11.7657|9.0465|15.0501|7.4817|11.4746|18.7147†|**21.0123**|\n|Nabf↓|0.0061|0.0101|0.0119|0.0066|0.0135|0.0121|0.0043†|**0.0018**|\n|DF↑|4.1873|4.4029|3.3765|5.3928|2.5307|4.0719|5.9137†|**8.8132**|\n|QSF↑|-0.2084|-0.1613|-0.3608|0.0546|-0.4742|-0.1946|0.2999†|**0.4947**|\n\n**(3)TNO Dataset**\n\n|Metric|TARDAL (CVPR 2022)|Seg_MiF (ICCV 2023)|DDFM (ICCV 2023)|Text-IF (CVPR 2024)|CCF (NeurIPS 2024)|Text-DiFuse (NeurIPS 2024)|GIFNet (CVPR 2025)|Ours|\n|:-----|:----------------|:------------------|:---------------|:------------------|:------------------|:--------------------------|:------------------|:----|\n|SD↑|40.1405|47.6092|34.2954|46.8663|36.8877|**51.2755**|40.4060|47.7260†|\n|AG↑|3.8912|4.2884|3.3802|4.6621|2.8500|3.0653|4.9954†|**7.2112**|\n|CC↑|0.4538|0.4657|**0.5307**|0.4614|0.5227†|0.4516|0.4966|0.4838|\n|SCD↑|1.5842|1.6577|1.7770|1.6856|1.7999†|1.6108|**1.8010**|1.7673|\n|EN↑|6.8079|6.9097|6.8496|**7.1878**|6.8925|7.1521†|6.9213|7.0975|\n|SF↑|10.6209|10.7205|8.5554|11.7515|7.3515|8.0038|13.3580†|**17.6254**|\n|Nabf↓|0.0350|0.0317|0.0443|0.0187†|0.0512|0.0602|0.0255|**0.0051**|\n|DF↑|5.0487|5.1762|4.3377|5.6968|3.4028|3.4862|6.0598†|**9.1041**|\n|QSF↑|-0.0922|-0.0382|-0.2628|-0.0179|-0.3740|-0.3407|0.0817†|**0.5264**|\n\n\n**(4)MFNet Dataset**\n\n| Metric   | TARDAL (CVPR 2022) | Seg_MiF (ICCV 2023) | DDFM (ICCV 2023) | Text-IF (CVPR 2024) | CCF (NeurIPS 2024) | Text-DiFuse (NeurIPS 2024) | GIFNet (CVPR 2025) | Ours |\n|:---------|:-------------------|----------------------|------------------|----------------------|--------------------|-----------------------------|--------------------|------|\n|SD↑|36.6004|36.5946|30.7512|41.3369|28.3691|**48.2688**|35.7542|46.3864†|\n|AG↑|3.3083|3.0624|2.7653|4.0105|2.7630|3.5280|4.3341†|**6.0428**|\n|CC↑|0.5681|0.5601|**0.6082**|0.5394|0.6077†|0.5262|0.5626|0.5454|\n|SCD↑|1.5599|1.4905|**1.6632**|1.5982†|1.5403|1.4753|1.5210|1.5466|\n|EN↑|6.6831|6.3983|6.4816|6.7777|6.3183|**7.0612**|6.3456|6.9013†|\n|SF↑|10.5243|9.1332|8.2793|12.2754|8.9107|10.9706|15.2457†|**17.4240**|\n|Nabf↓|0.0035|0.0062|0.0051|0.0031†|0.0041|0.0050|0.0035|**0.0007**|\n|DF↑|4.0647|3.4995|3.2343|4.6470|3.4424|4.0200|4.9290†|**7.2109**|\n|QSF↑|-0.0536|-0.2315|-0.2938|0.0379|-0.2492|-0.0471|0.2699†|**0.4782**|\n\n**Response to Weakness 2:** \n\nWe have added a new ablation study that systematically isolates and analyzes each individual constraint. As shown in the table below, the progression from the baseline (without any physical guidance) to successively adding these constraints yields clear and interpretable results:\n\nFrom A → B, introducing only $\\Phi_{\\text{heat}}$ significantly boosts perceptual indicators such as AG, EN, and SF, while also achieving the lowest Nabf. This demonstrates that enforcing thermal smoothness leads to tangible benefits even in isolation.\n\nFrom B → C, adding $\\Phi_{\\text{stru}}$ leads to further improvements in structure-related metrics such as SCD, confirming its effectiveness in preserving edges and local details.\n\nFrom C → Ours, incorporating the full $\\Phi_{\\text{con}}$ term further enhances global consistency and cross-modal balance, resulting in the best overall performance across nearly all metrics, including SD, AG, VIF, and QSF.\n\nThis layered ablation demonstrates that each constraint contributes uniquely to different aspects of fusion quality. More importantly, the full combination of all three constraints achieves the most balanced and robust results. \n\nThe best results are highlighted in bold.\n\n||$\\Phi_\\text{heat}$|$\\Phi_\\text{stru}$|$\\Phi_\\text{con}$|SD↑|AG↑|CC↑|SCD↑|EN↑|SF↑|Nabf↓|DF↑|QSF↑|\n|----------------------------|------|------|------|---------|---------|---------|---------|---------|---------|---------|---------|---------|\n|A|✗|✗|✗|43.1107|6.6147|0.5028|1.8086|6.9285|15.9481|0.0040|8.1256|0.3838|\n|B|✓|✗| ✗|42.0447|6.8718|0.5035|1.8076|7.0436|16.5597|**0.0029**|**18.3564**|35.0540|\n|C|✓|✓| ✗|41.2477|6.7049|**0.5080**|**1.8183**|7.0248|16.5085|0.0034|8.6943| 0.4319|\n|Ours|✓|✓|✓|**47.7260**|**7.2112**|0.4838|1.7673|**7.0975**|**17.6254**| 0.0051|**9.1041**|**0.5264** |\n\n**Response to Weakness 3:** \n\nWe appreciate your comment, which helped us correct this oversight. The descriptions of the three physical constraints in Equations (14)–(16) are indeed inaccurate. In fact, these constraints are applied sequentially in a cascaded manner. We have revised the manuscript to clearly state that the constraints are applied in order at each timestep:\n\nz0_hat --> $\\Phi_{\\text{heat}}$ --> z0_hat^heat --> $\\Phi_{\\text{stru}}$ --> z0_hat^stru --> $\\Phi_{\\text{con}}$ --> z0_hat^phys\n\n**Response to Weakness 4:** \n\nIn response to this suggestion, we have conducted a comprehensive study on the key hyperparameters used in our framework. Specifically, we divided them into three groups and performed targeted experiments to evaluate their influence on performance.\n\n(1) Analysis of $\\beta$ in Equation (5):\nWe evaluated the trade-off coefficient $\\beta$ (values: 0.1, 1, 10), which balances compression and information preservation in the variational bottleneck. The best performance occurred at $\\beta = 1$, achieving an effective trade-off between KL regularization and reconstruction. Smaller $\\beta$ led to under-regularization and instability, while larger values suppressed informative features.\n\n(2) Analysis of the physical guidance weights $\\lambda_{\\text{heat}}$, $\\lambda_{\\text{stru}}$, and $\\lambda_{\\text{con}}$ in Equations (14–16):\nA grid search over values {0.05, 0.10, 0.15} for each $\\lambda$ (with others fixed) yielded six combinations. The best result is with $(\\lambda_{\\text{heat}} = 0.05, \\lambda_{\\text{stru}} = 0.15, \\lambda_{\\text{con}} = 0.10)$, reflecting an effective balance: low $\\lambda_{\\text{heat}}$ prevents over-smoothing, high $\\lambda_{\\text{stru}}$ preserves structure, and moderate $\\lambda_{\\text{con}}$ promotes stable cross-modal fusion.\n\n(3) Analysis of the decay factor $\\gamma$ in Equation (17):\nWe tested $\\gamma = 1, 5, 10$ to examine its effect on time-dependent physical guidance. The optimal $\\gamma = 5$ maintained sufficient guidance while preserving flexibility. $\\gamma = 1$ was too weak, causing blurred structures; $\\gamma = 10$ was too strong, degrading performance by over-constraining the model.\n\nDue to the rebuttal's character limit, only a brief summary of the quantitative results is included here. And the complete ablation results are presented in the revised manuscript. If you would like to review the complete results, we would be happy to provide them in a follow-up.\n\n**Response to Question:**\n\nWe have sincerely and thoroughly addressed all the concerns you raised. The manuscript has been carefully revised based on your suggestions, and we look forward to your further feedback."}]

Author rebuttal:

Meta-review: This paper introduces a novel generative framework for infrared–visible image fusion inspired by principles of human cognition. Reviewers responded very positively, highlighting its strengths and requesting minor clarifications that were effectively addressed in the rebuttal. Consequently, all reviewers recommended acceptance.
Decision: Accept (spotlight)