Deno-IF: Unsupervised Noisy Visible and Infrared Image Fusion Method
Authors: Han Xu
Yuyang Li
Yunfei Deng
Jiayi Ma
Guangcan Liu
Venue: NeurIPS 2025 spotlight ()
Keywords: Unsupervised learning
image fusion
infrared
convolutional low-rankness
Primary area: applications

Abstract: Most image fusion methods are designed for ideal scenarios and struggle to handle noise. Existing noise-aware fusion methods are supervised and heavily rely on constructed paired data, limiting performance and generalization. This paper proposes a novel unsupervised noisy visible and infrared image fusion method, comprising two key modules. First, when only noisy source images are available, a convolutional low-rank optimization module decomposes clean components based on convolutional low-rank priors, guiding subsequent optimization. The unsupervised approach eliminates data dependency and enhances generalization across various and variable noise. Second, a unified network jointly realizes denoising and fusion. It consists of both intra-modal recovery and inter-modal recovery and fusion, also with a convolutional low-rankness loss for regularization. By exploiting the commonalities of denoising and fusion, the joint framework significantly reduces network complexity while expanding functionality. Extensive experiments validate the effectiveness and generalization of the proposed method for image fusion under various and variable noise conditions. The code is publicly available at https://github.com/hanna-xu/Deno-IF.

Deno-IF: Unsupervised Noisy Visible and Infrared Image Fusion Method
Han Xu', Yuyang Li', Yunfei Deng', Jiayi Ma², Guangean Liu
School of Automation, Southeast University, Nanjing, China 2 Electronic Information School, Wuhan University, Wuhan, China -jyma20101@gaall.comom {xu bxu_han, ( @@euu edu. en, 0at
guangcanliu@seu iusseu edu. cn
Abstract
Most image fusion methods are designed for ideal scenarios and struggle to handle noise. Existing noise-aware fusion methods are supervised and heavily rely on constructed paired data, limiting performance and generalization. This paper proposes novel unsupervised noisy visible and infrared image fusion method, comprising two key modules. First, when only noisy source images are available, a convolutional low-rank optimization module decomposes clean components based on convolutional low-rank priors, guiding subsequent optimization. The unsupervised approach eliminates data dependency and enhances generalization across various and variable noise. Second, unified network jointly realizes denoising and fusion. It consists of both intra-modal recovery and inter-modal recovery and fusion, also with convolutional low-rankness loss for regularization. By exploiting the commonalities of denoising and fusion, the joint framework significantly reduces network complexity while expanding functionality. Extensive experiments validate the effectiveness and generalization of the proposed method for image fusion under various and variable noise conditions, The code publicly available at htap::://gthuu.ccoom com/ com/banna hatm//annaxuuennoooo xu/Deno-DIe
Introduction
The widely used visible images, although rich in color and textures, are vulnerable to environmental factors, e.g.. illumination variations and occlusions. contrast, infrared images can capture thermal radiation information under extreme-light or obscured conditions while they often suffer from poor details and low quality. Therefore, visible and infrared image fusion aims combine the complementary and valuable information of these two modalities. The single fused image can retain the clarity and fine details of the visible image while incorporating the prominence of thermal targets in the infrared image for comprehensive representation. The fusion results can aid in both the human visual perception and subsequent widespread applications, such as object detection 22
semantic segmentation 20 43 autonomous driving 47
With the rapid development of deep learning, many learning- fusion methods have been proposed. These methods can be categorized into: methods based on auto-encoder (AE) 2 40
convolutional neural network (CNN) [1 21 33], generative adversarial networks (GANs)
42 18). Transformer [16] [3]. Mamba 29 and Diffusion [50] 33] 23). Despite the progress in
network architectures, these methods purely aim to preserve original information source images without distinguishing the information quality. In real-world scenarios, the quality of imaging is
adversely affected by challenging conditions (e.g., poor illumination) and the inherent constraints
"Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

Staristical Comparison PSNR, BRISQUE, and Parameters
DDFM (ICCV 2023) Pass 552 6GM
Pats CTNer 904
Demo-IF (cuns)
14M
CDIN (CVPS 2003)
Faoe 1966
Faxs| CTher $2.0
PrompiPasion (JAS 2025)
Pars 7.448
Visible (naisy)
Infrared (nolsy)
Fata (CTTN4)))56813
DRMP (ACM MM 2024)
TarBAL 2007)
Parx: 12159M
Tace C1NM
OuniiFase (TPAMI 2025)
Perp: 99. 99.15M
MccaPusion (CNPR 2023)
Pan (iCTN 52:35M
(CVPR 2024)
Fasiannoosster (UCV 2025)
Pure 3.50M
Pura (1CTNin::1100 STM
CTNet-DDFM
Deno-IF tours)
20
25
40
55
BRISQUE
Figure I: Comparison of Deno-IF with SOTA image denoising and fusion methods by: i) similarity with clean source images (measured by PSNR). ii) image quality assessment (measured by BRISQUE), and iii) complexity (measured by parameter numbers and represented by circle sizes). The high- and low-saturation circle regions represent parameters of fusion and denoising method 23 respectively.
of cost-effective multi-modal devices. These systems are prone to significant noise, stemming from degraded signal acquisition (especially in low-cost microbolometers and CMOS sensors) and ineffective embedded processing pipelines. Under interference conditions, there remain several critical challenges that need to be addressed to achieve robust high-quality fusion results [37 33
As most existing fusion methods are tailored for standard scenarios and ineffective at suppressing noise, an intuitive approach is to first denoise the source images before performing fusion. However, the framework taking denoising and fusion as independent tasks presents two limitations. On the one hand, denoising methods are typically deployed on a single source image. making it challenging to leverage complementary knowledge from the other modality. When denoising performance is limited, fusion methods are unable to handle the residual noise or over-smoothness, leading to its persistence in the fusion result, as shown in the result in Fig. T On the other hand, the mutually independent framework suffers from significant redundancy. The goal of image fusion is to extract vital and complementary information, which is then integrated into the fused images. Besides, image denoising aims to mine essential clean data from noisy inputs. From the perspective of essential information extraction and mining, their objectives are consistently aligned to some extent. The independent framework ignores the commonality, leading to redundancy as Fig. I There remains the potential to jointly realize denoising and fusion in a streamlined manner.
Although some degradation-awan image fusion methods exist [22 36), they are supervised methods aimed at learning the mapping from degraded source images to clean fused images. The key lies in the construction of large amount of noisy and clean source image pairs. Their denoising effectiveness limited by the fixed and limited mapping relationship between the constructed noisy and clean data, resulting in poor generalization. As a result, these methods are only effective for specific types of noise with particular variances. Facing various and variable noise encountered real-world scenarios, these methods are highly susceptible to failure, as shown in their performances in Fig. [
To overcome these issues, this paper proposes an unsupervised noisy visible and infrared image fusion method, termed as Deno-IF. It consists of two modules, including a convolutional low-rank optimization module and a joint denoising and fusion module. First, based on the convolutional low- rank property of high-quality data, the convolutional low-rank optimization module decomposes clean component from noisy input through convolution nuclear norm minimization in an unsupervised manner. Then, the joint denoising and fusion module takes noisy source images as input and directly outputs the fused image. The network includes both intra-modal recovery and inter-modal recovery and fusion, with self- and cross-modal attention to deal with complex individual-modal and complementary multi-modal information. The decomposed data provides the optimization guidance for the joint denoising and fusion module. Besides, convolution matrix- based regularization loss is

---

designed to further suppress noise in fused images. Thus, it can realize denoising and fusion jointly and generate clean fused images. The contributions are summarized as:
We propose an unsupervised noisy visible and infrared image fusion method. Without the super- vision of clean data, it can still realize denoising and fusion simultaneously with fewer network parameters, and is robust against various and variable noise conditions, ensuring effective across diverse scenarios.
For noisy source images, we design a convolutional low-rank optimization module. As clean data usually exhibits convolutional low-rankness, we introduce the convolution nuclear minimization to decompose clean data from noisy inputs. provides the optimization guidance for fusion network in the training phase.
For joint denoising and fusion, a Transformer-based network consisting of intra-modal recovery and inter-modal recovery and fusion is designed. It leverages self- and cross-modal attention to collaboratively strive to approximate the optimization guidance. Besides, convolution matrix-based regularization loss is designed to further suppress noise in fused images.
2 Related Work
Learning-Based Image Fusion Methods. These methods preserve the information in source images through the design of network architectures and loss functions. According to architectures, they can be divided into AE, CNN, GAN, Transformer, Mamba and Diffusion methods. AE- based methods design encoders and decoders for feature extraction and reconstruction, with traditional or learning- based (30) fusion strategies. CNN-based methods utilize end-to-end fusion [41] 6 and the key lies in pixel-level loss functions. GAN-based methods preserve unique information through adversarial games from the perspective of probability distribution while they always suffer from unstable training and model collapse [12 17 Transformer and Mamba-based based methods build long-range dependency to solve the limited receptive field of CNN [13 24 29 [5]. Diffusion- brassdd methods leverages the powerful generative properties of diffusion models establish stable fusion process [8 34
However, these methods are designed for normal scenarios and ignore degradations in real world. Facing degraded images, they fail to tackle degradations, reducing the quality and usability of results.
Degradation-Awar Image Fusion Methods. Some fusion methods suppress degradations and achieve fusion in unified network. Text-IF (36) leverages the text prompts with degradation information to guide the enhanced fusion. The network is trained with the manually constructed paired degraded and high-quality source images. DRMF 22 pretrains multiple degradation robust diffusion models and devises combination module to integrate priors. Similarly, the performance also depends on paired constructed data. They can handle different types of degradations including noise. However, since the introduced noise has fixed variance, it demonstrates limited generalization. These methods heavily rely on paired constructed data. This paper proposes an unsupervised method that requires only degraded data for training. We infer the clean component to guide the fusion network, significantly reducing the data demand and the number of network parameters.
3 Proposed Method
3.1 Problem Formulation
Mathematically, pair of observed noisy visible and infrared images (V, R} can be decomposed as:
V = L. + S.. R S...
(1)
where L,, L, is the clean data and S., S, is the noise. As clean data, L,, L, should be of convolu- tional low-rankness S S,, S, should be small-scale perturbations with limited overall energy and amplitude to ensure the convolutional low-rankness of L,, L,.
Taking noisy source images V. R input, the jointly denoising and fusion module generates high-quality fused image free from noise. Based on the decomposition assumption in Eq. (I). the goal is equivalent to making F contain vital information in L,, L, mined from V. R, respectively.
As shown in Fig. 2 the overall framework consists of the convolutional low-rank optimization module and the joint denoising and fusion module. The convolutional low-rank optimization module solves
3

---

Convolational Optimization (Training Phose)
Mi
M2
Iniraliration
Staget
Stage 1+1
Joint Denoising Fusion (Training Phose)
Intro-Medal
Transfrmer Bleck
Trandfnnner Block
Touraforma Block x.A
Optiniattion Caiddncc
F
Refinement
Transfomer
Block
Transforer Block
Transforer Block
Inter-Modo!
Recovery
Fassio
Axexe
Tranffrnner
Tranefomner Block
Tesraforma
Intra-Medal Recovery
Block
Dlock
Chamclatiie ooncaneration
Convolitional Low-Rank Optimization Module (Training Phase)
Figure 2: Overall framework of the unsupervised noisy infrared and visible image fusion method.
the ill-posed decomposition problem to provide precise guidance for network optimization. In the joint denoising and fusion module, network F takes noisy source image as input, consists of intra- modal recovery and inter-modal recovery fusion. Based on Transformer, intra-modal recovery leverages the self-attention mechanism to enhance restoration by capturing long-range dependencies within individual modality. Inter-modal recovery and fusion exploits cross-modal attention, enabling the joint restoration and fusion of complementary multi-modal information. F is optimized with the results of the convolutional low-rank optimization module to realize joint denoising and fusion.
3.2 Convolutional Low-Rank Optimization Module
For the decomposition problem in Eq. [ we take the infrared image R E Axx as example (subscript is temporarily omitted for simplification). To ensure the respective properties of L and the problem can be formulated as:
min
[A, B s.l. R-L+S. L.S
where the first term is based on convolution nuclear norm. which is the nuclear norm of its convolution matrix [8]. (-) is linear map from Rxx to k where kj (1 < K1 h) and l (1 12 < h) denote the kernel size in (+(.). ).). produces the convolution matrix of the input. -|+ and || || denote nuclear and Frobenius norms, respectively. 8 controls the trade-off between these terms.
Within the maximum posteriori (MAP) inference framework, and can be obtained by minimizing the following energy function:
ELL R L S|ß + aalAAA
(L)II- +
B||S|
(3)
where the first term fidelity term and the last two terms are regularization term denoting imposed priors over L and S. a also controls the trade-off.
With 19 releasing nuclear norm as:
X|| min A
s.t. X AB, B
(4)
A.B
2
the energy function can be rewritten as:
E(L.S,M.N) R L
S
M
2
N
AL)
+
(5)
2

---

where M E R ,NE Rm is a trade-off parameter.
This problem can be solved by iteratively addressing the subproblems related L, M,N. N. With / denotes the iteration step, the problem can be partitioned into the following four subproblems:
L arg min L
R L - S 1| + + (L) M
(6a)
S.
arg min
R
S11 + BISS
(6b)
M. = arg min
M + > (L.) - MN 112e.
(6c)
M 2
N₂ = arg min N
N (LL)) - M.N
(6d)
When t=1, initially set the variables as L1 = R. S is set to randomly distributed noise. M1 and N₂ are set to identity-lite like diagonal matrix. During the iteration process, the closed-form solutions for these four subproblems then be obtained
R - S (M,
L
7+1
where AZ()) is the Hermitian adjoint of Ak.
R R
S. 3+1
M. = L. 2,N.-IN,.
(8)
where I is a m x m identity matrix.
N₂ 27( M M.) M.)-MM M, A&
(9)
For the visible image pxxwxx the primary distinction infrared data lies in three channels. It results in slight differences in the construction of convolution matrix and the shapes of M,N.
Specifically, (V) is linear map from Axxxx Rhonexkakak, where ky (1 S ky S h). ka (1 la < w) and ks (1 < ka S c) are the kernel size. M E Rhwexx and N E TRn
3.3 Joint Denoising and Fusion Module
Network Architecture. Directly denoising multi-modal source images as a whole without distinction will overlook the modal differences and noise characteristics between multi-modal images, posing greater challenges to F. Thus, the fusion network F comprises two key components: intra-modal recovery and inter-modal recovery and fusion. In one source image, the modality is unique and the noise characteristics are consistent. We first perform intra-modal feature recovery visible and infrared domains, respectively. Then, the inter-modal recovery and fusion integrates information across different modalities while effectively handling the complex noise characteristics among these modalities. The overall network is named as 12Former. The output is the clean fused image F.
For the two components in F, we adopt the Transformer block (39 for intra- and inter-modal feature extraction and integration. In intra-modal recovery, we respectively extract features from V, R as f,, f,. In each block, the intra-modal multi-Dcome head transposed attention (Teer) generates query, key and value projections for f.,, fr as:
QQ,, K. V.) TY. (f.), (Q = TR T (f,).
(10)
Then, the output visible feature maps are f. Softmax K. V. + +fe, where ç is learnable scaling parameter. Infrared features are modulated similarly. The features are passed through gated- -Dcony feed-forward network for controllable transformation. Multiple blocks generate multi-scale features.
In inter-modal recovery and fusion, the visible and infrared features the smallest scale are concate- nated, fed into convolutional layer for channel reduction and upsampled. With f, and f,, f, input, the inter-modal multi-Dcony head transposed attention (Taa) generates projections as:
(Q,: Ky, v,} = Tira (W (f,, Er,
(11)
where W is a convolutional layer for channel reduction. The final output features can be obtained as f, Softmax( K, Q VV, + W (f,, fy, The fused features of the original scale are finally passed through some blocks for refinement and convolutional layer to generate the fused image F.
5

---

Loss Functions. From the aspect of network optimization, clean components derived with convo- lutional low-rank priors serve as physics-driven prior injector regularize fused images, and ii) implicit teachers that enable knowledge distillation of denoising priors into the fusion network. It enables simultaneous noise suppression and information fusion. Thus, the loss functions of consist of some data fidelity terms and a regularization term.
Data Fidelity Terms. Sec. 3.2 obtains the estimation of Ly and L,. The data fidelity terms constrain the fused image F to keep similarity with Ly and L, from multiple perspectives to preserve the scene information, including intensity, gradients, and chrominance components.
The intensity loss makes capture proper intensity distribution. One target is to preserve the thermal prominence in L, Considering the low thermal radiation regions in L, and low-light regions in Ly, the other target is to preserve the more prominent information. The intensity loss is denoted as fin F [L, I,1] where L. Fe the Y channels of Ly F in YCbCr space, respectively.
The gradient loss preserves the prominent textures in Ly: I, to present more contents, We generate gradient mask Mg considering the gradient relationship between L L, When Fi.
Ui.j
møi.j = 1 where V is Laplacian operator, i and j indicate the spatial position. Otherwise, mai. 0. The loss constrains the gradient similarity as:
Lg VVF my +1 mg). VL,] 111.
(12)
The chrominance loss preserves the chrominance information in L, into F. L,, are translated into YCbCr space and the constraint is performed on chrominance channels
Lehr
| - - + || Le
(13)
Regularization Term. In the optimization process F, the performance gap in intra-modal recovery and the residual and cumulative deviation in inter-modal recovery and fusion will affect performance. To further correct deviations, the convolutional low rank of fused image is regularized as:
Lrank = [F)le(F)lls
(14)
where (F) computes the convolution matrix of F. Ac(.) is a linear map from Rexxxe to hy kakk (defined at the end of Sec. 3.2 The nuclear norm imposes a low-rank constraint on the convolution matrix of F, thereby reducing residual noise in F by mining its intrinsic structure.
With
X, 1), x to control the trade-off, the final loss function is defined as:
L = Lin + Abg + nLech + K&
(15)
4 Experiments and Results
Datasets and Implementation. Existing datasets are collected for the target of high image quality, thus relying on cost cost-prohiin prohibiioeee imaging systems to ensure high signal-to-noise ratios. As there are no publicly available noisy infrared and visible datasets for direct use, We simulate real-life noisy source images by injecting two representative types of noise with various levels (additive Gaussian noise and multiplicative speckle noise) into clean data (27 46). The clean data is not used during training. We train De on 2120 pairs visible and infrared images across two datasets, including LLVIP (5) and M3FD [9. For the introduced noise, a randomly distributed in [10, 50). In the training phase, images are randomly cropped into patches of 128 x 128 for training. The evaluation is performed on image pairs introduced Gaussian speckle noise with randomly distributed variance.
In convolutional low-rank optimization module, h and w are 128, c 3. Kernel size of A₂ are ki, 12 = 12, kig 2. m, n 256. We perform 30 iterations for each patch. For parameters in Eq. (5). 3= 2. a, are empirically related to data characteristics. As an unsupervised method, for a patch we roughly estimate its noise level as n E (x)) (xG()) where G(.) is Gaussian blur. 200n and 80n for visible and infrared data respectively. B 15n. These manually designed settings stem from the consideration of i) principled trade-off for adaptive regularization (strong for high-level noise for noise suppression, weak for low-level noise to preserve details); and ii) computational efficiency for an optimal efficiency-accurace balance.

---

LAVP
Gansdaa
Speake Vale
HITD
Figure 3: Qualitative results when both source images are subject to various-level Gaussian/speckle noise on LLVIP and M3FD datasets.
Table 1: Quantitative results when both source images suffer from various-level Gaussian or Speckle noise (bold: optimal, underline: suboptimal).
Gaussdan
LLVIP Dataset
MEED Dataset
Metries
SS1M
PSNKT
FSIM
CC
BRISQUEL
SSIM?
PSART
FSIM
CCT
BRISQUE
CT-Tar
142
15.60011464
0.600
19.519-1.577
14.997:2 0:460-00143
0.75660005
0.513601195
24889997713
CEACDD.
0:46610.006
15881,1356
0.098 0.008
33.929 20.019
D.466 (L140
15.07612.700 789
0.772.0001
0.50110.226
42.196__14.155
CT-DDDMM
1474
167882
0.815
0.736-007
10.624:16 16 966
0.489 1.165
15:92542443
0.767.0001
0.587
35.771
C..MMeta.
0.02:0000
15.27421.193
0,604:0045
0.062:0033
24,507 12
0,533 0.152
24.60612660
0.675:0068
0.54810.19
14.430:5.80
CT:-Pusion.
0,27000.155
11669196624
0,760x9042
0.679:00078
26.381=11A44
0.459:00004
10.6184100
0.744=0047
0,565e0.155
16.474:11.222
CT.-Prompt.
043320019
15811
0.807:0009
0,702:00075
36.876:199.8 147
0:474
15.007 079
0.777-000
0.470
25.837:11550
DRMP
0.148ea000
11.899:12222
0.691
0:489=0156
67.922=11.402
0.260:0.121
13.462+1 625
0.738=0004
0.33650.219
44.186-111117
Teat-FF
0:208:0068
14
0,730
0.622:0011
54.482.10611
138837,290
0.710.000k
37.228:8341 0:40410:009
OmnilFuse
0,577a000
1264260/221
0.758:0105
0.442:0099
41.046-31466
0.540:00.06
14.488:2.119
0.763:0001
0 0:459:0.111
46.787:66866
Demo-IF
0616:4064
17.070=1426
0.811+0003
0.738-8074
16.725-4491
0
15.637
0.788-4822
0.56340..05
24.298:7.005
Speckle
LLVIP Dataset
MESE Dataset
Metries
SSIM
PSNR
FSIM
OC
BRISQUEL
SSIM
PSART
FSIM
CCT
BRISQUE
CL+Iac
0423:00023
15.125
0.776201044
0.87:00022
13.375
0.553=0.120
15.3581290)
0.775
13.950:9220 0.553
CE-CDD.
0,1190009
15.92711515
0,78959005
0.681=0065
13.497=134496
0.539=0.345
15,90642500
0.789=0041
0.55840.215
25.231=14:022
CT-DDDFM
0474::0006
16191
0.808
0.733
12.897:217-57 577
0.584_.
162250
0 0:60600077
18.320 767
CT.MMeta.
0538.0002
15:102:1:220
0.607.1043
0.60188015
10.860-9.511
0.615.00115
25.60612555
0.710:0006
0.61110.164
10.412:4.387
CT.-Pussio.
0,538
11.909.060
0,757.0002
29.446
0.516
0.774.000
0.61210.100
21.17819.900
CT.-Prompt.
0.3815008
15.112e13133
0,75720025
0.811:0066
48,250::12442
01522:00144 0.146
15.881:2447
0.784::004
0,521:0.244
21,557=:22225
DRMP
0:675
12411611377
0,722+0001
0.567=0137
21.
21.406=10.566
0.519:0:100 1000
14.14762261
0.790=00005
0. 0.42340.246
43. 150
0440000541
1305331111
0,75910099
0.554_.0158
15,041177.00
0.780g
0:49610.715
18:908-10.34
OmnilFuse
0,47950022
1285920.75
0,743:0017
0.065:0074
49.696.24.410
0.585:00.104
15,221:2.089
0.767=0055
0.52510.213
44.85022.131
Deso-1F
0.633,0882
16.7281..70
0.805 19122
0.73M 8.065
9,280.55900 5,918
RU656 0.875
16.825
0,513 8.826
0.648 0.155
16.514:8400
In the joint denoising and fusion module, 12Former is updated with Adam Optimizer with batch size as 4 and epoch as 80. Learning rate is 2c-4 with exponential decay. Syyee--araamttrs are X = 1e3, r = 30. K increases during training and equals the multiplication of le-6 and epoch. Numbers of blocks in Fig. are Ly, L3 2, L2 = 4. Experiments are conducted on an NVIDIA 3090 GPU.
Comparison Results. We consider two typical types of noise (additive and multiplicative noise) during imaging process, e.g., Gaussian and speckle noise in source images. For noisy source images, we compare Deno-IF with both the combination of SOTA denoising and fusion methods and degradation-aware image fusion methods. As some fusion methods (TarDAL 19 CDDFuse [49], DDFM (50]. MetaFusion (48, FusionBooster D, and PromptFusion 100) cannot handle noise, CTNet 25 (supervised denoising method) is used for pre-denoising before these fusion methods are performed. The rationale of selecting CTNet for comparison is two-fold: i) validating the unsupervised generalization of the proposed method against the supervised denoising performance of CTNet; ii) validating the improvements of architecturally relevant frameworks and objectives as CTNet also designs Transformer-based network. The supervised degradation-awan fusion methods (DRMF 22. Text-IF (36,, and OmniFuse 44 directly fuse noisy source images.
Qualitative Results. Qualitative results where both source images suffer from various-level Gaus- sian or speckle noise across two datasets are shown in Fig. 3 First, as Deno-IF jointly realizes denoising and fusion, it avoids the residual noise in competitors caused by the limited pre-denoising performance. Second, as an unsupervised method, Deno-IF IF can infer clean data from source images which both suffer from noise and present clear fused images. On the other hand, it shows robust
7

---

Cihe-Cio.
Ciha-tors
Tree-F
Dre-dif
Figure 4: Qualitative results when only one image is subject various-level noise.
LANIP datant: Halll lahaare
LESIP viall tnflend
(odlyy jueirt
Figure 5: Quantitative results in difference situations of noise presence on two datasets (see Supple- mentary Material for results in specific situations).
performance across various noise types and levels, addressing the low generalization of existing supervised denoising and fusion methods. Finally, the inference on clean data in Deno-IF based on convolutional low-rankness, avoiding excessive distortion of meaningful scene content in competitors.
metrics (evaluating similarity between fused and source images) and statistics-based metrics (measuring image characteristics, e.g., SD, SF, EN, etc.). However, when fusing noisy images, statistics-based metrics may produce misleading results as noise may artificially inflate these values (c.g., higher EN/SF caused by noise rather than meaningful information). Thus, in this work, we prioritize similarity-baa based full-reference metrics (i.e., SSIM (28). PSNR, feature similarity index (FSIM) 45 and correlation coefficient (CC)) and a quality-bart based no-reference metric BRISQUE 14 to evaluate denoising and fusion performances 30 image pairs.
Quantitative Results. Traditional fusion metries fall into similarity-
As reported Tab. T Deno-IF achieves optimal performances on most metrics. The results on full- reference metrics indicate that our results show high similarity with clean source images. The results on the non-reference metric indicate that our results have less distortion. Finally, the comprehensive results and small variances indicate the generalization of Deno-IF for various scenes, noise types, and noise levels.
Validation on Single-Modal Noise. To validate the generalization under different noise presence situations especially specific-modal noise, we perform experiments where only one source image suffers from noise. We take source images with Gaussian noise for example and qualitative results are shown in Fig. [ On the one hand, Deno-IF effectively removes noise in infrared or visible modalities without being affected by modal differences, avoiding residual noise in results. On the other hand, indicates the generalization of Deno-IF different noise presence situations. As reported in quantitative results of each situation in Fig. 5 Deno-IF shows the optimal results in SSIM, PSNR and FSIM, and suboptimal results CC and BRTSQUE total amount. From the perspective of variance across situations and datasets, Deno-IF achieves advantageous and balanced performances.

---

A - 0.1
# a1
e m 0.2
0.1 0.5
1.5
=
15
(b) Impact f on decomposition when 1000 y 1.5
Vistile (oniog)
Infrrure (hougg)
P
wio intre-mndal recovery
(a) Impact of the estimation of clean component L
Wio direct Sasion (c) Qualitative results of the ablation experiments
wilr
W/l Fount
Deno-Ii (ouns)
Figure 6: Impacts of decomposition parameter settings and results of ablation experiments.
Table 2: Quantitative results of ablation study and performances dealing with different-level noise.
PSN!
SNDU
BUDQUE
DR34
Text.
Dees.
DRMI
Deas
DRMI
Tex:
Deas
DRMI
Deas.
DRMB
16:35
0.730
6:300
0.70
8557
77189
8:352
0775
0777
(a) Results of ablation study.
(b) Results on dealing with different-level noise.
Optimization Parameter Settings. We validate impacts of a, 8, in Eq. (5) on the estimation of clean component L. We first analysis impacts of a, y and then analyze the impact of 3 with fixed a, Taking visible data for example, when 8 2, Fig. [6 shows results with varying a, 7- a constrains the Frobenius norm of M, N. constrains similarity between AL( and MN. Small yy fails to ensure similarity. Regardless of varying o, tends to be noisy. As 7 increases, Av, tends to be low-rank for clarify. Excessively large y disrupts the similarity between L + S and noisy image, leading to brightness and content distortions. With appropriate 7, a small a leads to texture distortion. A large leads to smoothing and hazy appearance. By carefully tuning the details can be adjusted for balance. Then, the decomposition results with varying 3 are shown in Fig. (6p. When 3 is small, the constraint S is weak and many contents mistakenly decomposed into S. When / is large enough, contains little information, resulting in residual noise in L.
Ablation Study. We validate some architecture designs and loss functions. For architectures, we consider: i) intra-modal recovery in 12Former. We remove it and modify 12Former a Restormer-like structure with similar parameters. ii) direct fusion. Rather than learning residual image to noisy inputs, 12Former directly generates fused image. learns a residual image F, to a pre-fused image F, as fused image remove the gradient loss fg and regularization term Lrank As in Fig. To. F, contains noise. Without intra-modal recovery, the result shows diagonal effects. learning a residual image to Fp. the unstable training process results in slight intensity and color casts. The lack of C, results in blur contents. Without Lyank, the result cannot infer more contents according to convolutional low-rank property. Deno-IF generates clear results with more contents. The results in Tab. 2a are also consistent with qualitative results. Without Ly. shows the worst performance. By introducing intra-modal recovery, direct fusion, and Lrank. method achieves the optimal performance among all the settings.
F, + F,, where F, ((max(()) R), yCb vCr). ). For loss functions, we
Performances on Different-Level Noise. Experiments on different-level noise Gaussian noise (as a representative) are reported in Tab. 2b As a increases, the performances of the competitors increasingly challenging trend for recovering under more severe noise, method shows smaller fluctuations, showing its robustness.
significantly decrease on most metrics. Although

---

Vidble (molsy)
Infrared Ceelgy)
DRMP
Teue-IF
OrentPasee
Be loarg
Vidille
Infrand
CLACDD
CL.-DDMM
CT.4Mea
CT-Fiienn
Test-F
OmaiiFux:
Dens-F (ours)
(a) Qualitative performances when dealing with different-level noise.
(b) Results of object detection with YOLOv8.
Figure Results different-level noise and external verification on high-level task.
Table 3: Computational efficiency comparison of different methods.
Mathods
Ybe.
CID),
DDFM Mets.
Fision Primpt CT+ CT~CMD. CTAIDFM CLMMita CT--miin CTL+Prampt. DRMF
Text-If'
Omn. Bhno-I
Para
215.12
98.15
FLOPs (G))
91
548
5221
159
341
36577
41685
42152
46524
41765
41845
78481
4576
1118
5471
115
External Verification on High-Level Task. The denoising and fusion performances are externaly validated by subsequent high-level task-objec detection. In Fig. (p. it is challenging accurately detect some targets single noisy visible/infrared image. By denoising and fusing noisy source images, some competitors can boost detection accuracy while some methods exhibit notable mis- classification. Our result eliminates noise interference and preserve essential information, improving both detection accuracy and precision. Quantitative results reported in Supplementary Material.
Efficiency Comparison. The efficiency is evaluated by parameter numbers, FLOPs, and runtime. Due to the large parameters, high computational complexity. and runtime of denoising method CTNet, we also report the efficiency of pure fusion methods. As reported in Tab. 3 our method shows higher efficiency than the competitors. It also achieves simultaneous denoising and fusion with efficiency that is comparable or even superior to those of pure fusion networks.
Conclusion
This paper proposes unsupervised noisy visible and infrared image fusion method, addressing the limitations of existing methods that cannot handle noise interference or rely on paired data restricted by noise characteristics. The proposed network, featuring convolutional low-rank optimization module and a joint denoising and fusion module, climinates data dependency and improves generalization while reducing complexity. Experimental results demonstrate its effectiveness and robustness.
Acknowledgments and Disclosure of Funding
This work was supported by the National Natural Science Foundation China (NSFC) under Grant U21 and 62502085, the Natural Science Foundation of Jiangsu Province of China under Grant BK20241 280, and the Start-up Research Fund of Southeast University under Grant RF1028623006 and RFI 1F1028624061.
10

---

References
[l] C. Cheng, Xu, X.-J Wu, H. Li, X. Li, and Kittler. Fusionbooster: A unified image fusion boosting paradigm. International Journal of Computer Vision, (33(5)3041-30555 2025.
[2] F. Dufthauss, N. A. Vien, H. Ziesche, and G. Neumann. Fusionvae: A deep hierarchical variational autoencoder for rgb image fusion. In Proceedings of the European Conference on Computer Vision, pages 674-691. Springer, 2022.
[3] Y. Gong, J. Lu, Liu, Z. Li, Jiang, Gao, and Wu. Sifdrivenet: Speed and image fusion for driving behavior classification network. IEEE Transactions on Computational Social Systems, 2023.
[4] H. Huang, W. He, H. Zhang. Y. Xia, and L. Zhang. Stfdiff: Remote sensing image spatiotemporal fusion with diffusion models. Information Fusion, page 102505, 2024.
[5] X. Jia, C. Zhu, M. Li, W. Tang, and W. Zhou. Llvip: A visible-infrared paired dataset for low-light vision. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3496-3504, 2021.
[6] H. Li, X.J. Wu, and J. Kittler. Rfn-nest: An end-to-end residual fusion network for infrared and visible images. Information Fusion, 73:72-86, 2021.
[7] X. Li, X. Li, H. Tan, and J. Li. Samf: small-area-aware multi-focus image fusion for object detection. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pages
IEEE, 2024.
[8] G. Liu and W. Zhang. Recovery of future data via convolution nuclear norm minimization. IEEE Transactions on Information Theory, 69(1):55-6665, 2022.
[9] J. Liu, X. Fan, Z. Huang, G. Wu, R. Liu, W. Zhong, and Z. Luo. Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pages 5802-5811, 2022.
[10] J. Liu, X. Li, Z. Wang, Z. Jiang, W. Zhong, W. Fan, and B. Xu. Promptfusion: Harmonized se- mantic prompt learning for infrared and visible image fusion. IEFE/CAA Journal of Automatica Sinica, 1233)502-555, 2025.
[11] J. Ma, H. Xu, J. Jiang, X. Mei, and X.-P. Zhang. Ddegan: A dual-iiscriminator conditional generative adversarial network for multi-resolution image fusion. IEEE Trunsactions on Image Processing, 49:4980-4995, 2020.
[12] J. Ma, H. Zhang, Z. Shao, P. Liang, and H. Xu. Ganmec: A generative adversarial network with multiclassification constraints for infrared and visible image fusion. IEEE Transactions on Instrumentation and Measurement, 70:1-14, 2020.
[13] Ma, L. Tang, F. Fan, J. Huang, X. Mei, and Y. Ma. Swinfusion: Cross-domain long-range learning for general image fusion via swin transformer. IEEE/CAA Journal of Automatica Sinica, 9(7):1200-115 1217, 2022.
[14] A. Mittal, A. K. Moorthy, and Bovik. No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing, 21(12):4695-4700 2012.
[15] S. Peng. X. Zhu, H. Deng, L.-J. Deng, and Z. Lei. Fusionmamba: Efficient remote sensing image fusion with state space model. IEEE Transactions on Geoscience and Remote Sensing, 2024.
[16] L. Qu. S. Liu, M. Wang, and Z. Song. Transmef: transformer-based multi-exposure image fusion framework using self-supervised multi-task learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 2126-2134, 2022.
[17] D. Rao, T. Xu, and X.-J. Wu. Tgfuse: infrared and visible image fusion approach based on transformer and generative adversarial network. IEEE Transactions on Image Processing, 2023.
11

---

[18] Y. Rao, D. Wu, M. Han, T. Wang, Y. Yang, T. Lei, C. Zhou, H. Bai, and L. Xing, At-gan: generative adversarial network with attention and transition for infrared and visible image fusion. Information Fusion, 92:336-349, 2023.
[19] P. Sprechmann, A. M. Bronstein, and G. Sapiro. Learning efficient sparse and low rank models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(9):1821-188 18333,,3111111 2015.
[20] L. Tang, J. Yuan, and J. Ma. Image fusion in the loop of high-level vision tasks: A semantic- aware real-time infrared and visible image fusion network. Information Fusion, 22:28-42, 2022.
[21] I.. Tang. J. Yuan, H. Zhang. X. Jiang, and Ma. Piafusion: A progressive infrared and visible image fusion network based on illumination aware. Information Fusion, 83:79-92, 2022.
[22] L. Tang, Y. Deng, X. Yi, Q. Yan, Y. Yuan, and J. Ma. Drmf: Degradation- multi- modal image fusion via composable diffusion prior. In Proceedings of the ACM International Conference Multimedia, pages $546-8555 2024.
[23] L. Tang, C. Li, and J. Ma. Mask-difuser: A masked diffusion model for unified unsupervised image fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.
[24] W. Tang, F. He, Liu, Y. Duan, and T. Si. Datfuse: Infrared and visible image fusion via dual attention transformer. IEEE Transactions on Circuits and Systems for Video Technology, 33(7): 3159-3172, 2023.
[25] C. Tian, M. Zheng, W. Zuo, S. Zhang. Y. Zhang, and C.-W. Lin. A cross transformer for image denoising. Information Fusion, 102: 10204,, 2024.
[26] D. Wang, J. Liu, R. Liu, and X. Fan. An interactively reinforced paradigm for joint infrared- visible image fusion and saliency object detection. Information Fusion, 08:101828 2023.
[27] Y. Wang. X. Geng, W. Huang, B. Huang, and M. Gong. Generator identification for linear sdes with additive and multiplicative noise. Advances in Neural Information Processing Systems, 36: 64103-64138, 2023.
[28] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4)6600-612, 2004.
[29] X. Xie, Y. Cui, Tan, X. Zheng, and Z. Yu. Fusionmamba: Dynamic feature enhancement for multimodal image fusion with mamba. Visual Intelligence, 2(1 311):37, 2024.
[30] H. Xu, H. Zhang, and J. Ma. Classification saliency-based rule for visible and infrared image fusion. IEEE Transactions on Computational Imaging, 7:824-836, 2021.
[31] H. Xu, J. Ma, J. Jiang, X. Guo, and H. Ling. U2fusion: unified unsupervised image fusion network. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(1):502-518, 2022.
[32] H. Xu, J. Yuan, and Ma. Murf: Mutually reinforcing multi-modal image registration and fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45810)61214--1216, 2023.
[33] H. XU, X. Yi, Lu, G. Liu, and J. Ma. Urfusion: Unsupervised unified degradation-robust image fusion network. IEEE Transactions on Image Processing, 34:5803-5818 18, 2025.
[34] B. Yang, Z. Jiang, D. Pan, H. Yu, G. Gui, and W. Gui. Lfdt-fusion: latent feature-guided diffusion transformer model for general image fusion. Information Fusion, 113: 10:609,,,, 2025.
[35] X. Yi, L. Tang, H. Zhang, Xu, and Ma. Diff-if: Multi-modality image fusion via diffusion model with fusion knowledge prior. Information Fusion, 102450,, 2024.
[36] X. Yi, H. Xu, H. Zhang. I. Tang, and J. Ma. Text-if: Leveraging semantic text guidance for degradation-awar and interactive image fusion. In Proceedings of the IEFF/CVF Conference on Computer Vision and Pattern Recognition, pages 27026-27035, 2024.
12

---

[37] X. Yi, Y. Ma. Li. H. Xu, and J. Ma. Artificial intelligence facilitates information fusion for perception in complex environments. The Innovation, 2025.
[38] Yue, L. Fang, Xia, Y. Deng, and J. Ma. Dif-fusion: Towards high color fidelity in infrared and visible image fusion with diffusion models. IEEE Transactions Image Processing, 2023,
[39] S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, and M.-H. Yang. Restormer: Efficient transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5728-57399 2022.
[40] G. Zhang, R. Nie, and J. Cao. Ssl-waeie: Self-supervised learning with weighted auto-encoding and information exchange for infrared and visible image fusion. IEEE/CAA Journal of Automat- ica Sinica, 999)16694-1997, 2022.
[41] H. Zhang, H. Xu, Y. Xiao, Guo, and J. Ma. Rethinking the image fusion: A fast unified image fusion network based on proportional maintenance of gradient and intensity. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 12797-12804, 2020.
[42] H. Zhang, J. Yuan, X. Tian, and J. Ma. Gan-fm: Infrared and visible image fusion using gan with full-scale skip connection and dual markovian discriminators. IEEE Transactions Computational Imaging, 7: 1-1134-1147, 1147, 2021.
[43] H. Zhang, X. Zuo, H. Zhou, T. Lu, and J. Ma. A robust mutual-reinforcing framework for 3d multi-modal medical image fusion based visual-semantic consistency. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 7087-7095, 2024.
[44] H. Zhang, L. Cao, X. Zuo, Z. Shao, and J. Ma. Omnifuse: Composite degradation-robur robust image fusion with language driven semantics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(9):7577-777
2025.
[45] L. Zhang, L. Zhang, X. Mou, and D. Zhang. Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing, 20(8):2378-2386, 2011.
[46] M. Zhang, K. Yue, B. Ji Guo, Y. Li, and X. Gao. Single-frame infrared small target detection via gaussian curvature inspired network. IEEE Transactions on Geoscience and Remote Sensing, 62:5005013, 2024.
[47] X. Zhang. L. Wang. G. Zhang, T. Lan, H. Zhang, L. Zhao. J. Li, L. Zhu, and H. Liu. Ri-fusion: 3d object detection using enhanced point features with range-image fusion for autonomous driving. IEEE Transactions on Instrumentation and Measurement, 72:1-13, 2022.
[48] W. Zhao, S. Xie, F. Zhao, Y. He, and H. Lu. Metafusion: Infrared and visible image fusion via meta-feature embedding from object detection. In Proceedings of the IEEE/CVE Conference on Computer Vision and Pattern Recognition, pages 13955-13966 2023.
[49] Z. Zhao, H. Bai, J. Zhang, Y. Zhang, S. Xu, Z. Lin, R. Timofte, and L. Van Gool. Cddfuse: inrellationdriveee
feature decomposition for multi multi-modality image fusion. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pages 5906-5916, 2023.
[50] Z. Zhao, H. Bai, Y. Zhu, J. Zhang, S. Xu, Y. Zhang, K. Zhang, D. Meng, R. Timofte, and L. Van Gool. Ddfm: denoising diffusion model for multi-modality image fusion. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8082-8093, 2023.
[51] A. Zheng, J. Liu, Z. Wang, L. Huang, C. Li, and B. Yin. Visible-infrared person re- re-identification via specific and shared representations learning. Visual Intelligence, 1(1):29, 2023.
13

---

Technical Appendices and Supplementary Material
Details of Transformer Block and Variation of Losses under Different Network Settings
The network architecture of the Transformer Block in Fig. Qis is shown in Fig. where the left part is multi-dconv head transposed attention and the right part the gated dcony feed-forward network.
In the ablation study introduced in Sec. we perform the experiments by removing the intra-modal recovery and learning residual image to pre-fused image (w/o direct fusion). The variation of losses under these settings during the training phase is shown in Fig. 8p. By comparison, the 12Former in Deno-IF achieves both faster convergence speed and higher convergence accuracy.
501 CaF
Trakning
Variation of losses during trainiing under
[6] architactore of Transfrmer Block
dilferel arbwock settings
Figure 8: Details of Transformer block and variation of losses under different network settings.
2. Quantitative Results of Generalization in More Situations of Noise Presence
The quantitative results on five metries across two datasets when fusing noisy visible and clean infrared images; ii) clean visible and noisy infrared images are reported in Tabs, 4 aad respectively. In these situations, our method can still achieve optimal or comparable performances, indicating its generalization for various situation of noise presence.
Table 4: Quantitative results when fusing noisy visible and clean infrared images (bold: optimal, underline: suboptimal).
Datasets
LLVIP
MEED
Metries
SSIM
PSNR
ESTM:
CC1
BRISQUE!
SSIM
PSNR
FSIM
CC1
BRISQUE]
CI-Tac
0.370.0115
15.14411441
0804
25/633_5444
0.68
15.423
0.809
0.512_.0109
21 1
CE&CDD.
049910.50
15,005a1510
0814:0025
0.712:0008
39.863:19
0.674:0.125
15.609:2.06
0812:0144
0.520:00214
27.94526.977
CE-DDEM
0.90=00112
16.91541359
0.2550
0,753-086
32.374 254
0.699
15 15.909-23308
0.814:00055
0612:00119
29 13366.1100
CT-MMeta.
B494=0072
15.34841.25
0.077:0035
0.687-0078
30.094:7.736
0.642:00114
14.920224410
0,729
0.509-0185
16.723:9
CT--Pusio
1,309
11.75650003
0.720
16.910:132231
0.636:00075
0.605-0.111
CT.-Proppt.
044490014
15.743114400
0814100s
0.713.0009
37.600.1740 969
0.699_0.72
15.961_2.99
0.80600945
0.474_0:355
26.715
DRMP
u.136:00045
11.124:0.588
0.03:00019
0,489:00.50
75.159-4.615
0.346:00:44
13.752:2.144
0.739:0.44
0.351=0222
15.136:15254
0,36500112
14.77
0.776:0.022
0.696:00771
15.487-8117
0.660=0:117
15,40053.055
0.81420044
0.465-02225
OmniFuss
0456:0001
12,252:0800
0.748:0014
0.654g000
40.314_2.416
0:667-0001
15.255_2.40
0:801amm
0'49_00117
53.26415.029
Deso-1F'
0,635 (884)
17.104:1.001
0.827-9/22
0.749.00006
43.779
0.091:0082
15.973.1446
0.822:0012
27. 27.164:5.110 0.592..0.144
Table 5: Quantitative results when fusing clean visible and noisy infrared images.
Datavets
LIXIP
M3PD
Metries
SSIM
PSNR
FSIM
CC1
BRISQUEL
SSIM
PSNR1
FSIM:
CCT
BRISQUE]
CL+Tac
0,34150138
34.610a1300
0.77240021
0.687=0077
20.811:1:946
0,511=01177
15.467a2244
0.778=0026
0.60960.161
19.693:9930
CL+CDD.
0.340a01154
3419221135
0.785
0339900075
41.55:111512
0.54
10,04882244
0.79
0.615500188
35 35.0300:17.919
CT-DDEM
0411990117
16066e1 220
0.803
0.752-9073
36.578=11.318
0.555
16,21 272
0,503=0042
0.675:9.145
30.017:15.90
CT.MMtt.
0:543,0000
14.70121189
0.68510m9
0.676.0084
18.320 10.909
0.608
15.512.2
0.006.0045
0,64210115 190
10.203.7004
Flsioo.
0,42220022
11.31620652
0.75720109
0.44200012
31.142:55061
0.503
11.081100885
0.754:0057
0.66310.190
18.54415.14
CT..Prompt.
1.332
1484511331
0,782,802
0.707.0011
47.847.112100
0.550 0.145
16,92612511
0,795 1000
0.57610.208
25.187_.14566
DRMP
0,5000055
13.508:1.200
0.73920027
0.001:0135
25.842:7.661
0
15.67542.10
0.:00=0000
0.452:0.221
46.535-10449
0,566sntes
15.097e1.355
0,770=0
22:293-77481
0,660:0.113
16.5054244
0.795
0.561:00211
15.388666241
OmmiFFuse
0.54
12.721.0412
0.754.0m1
0,688
36,807_4.11
0.688_(059)
15,70012 124
0,506_po0m
0.54110::15
48. 140
Deso-1F'
0.660:4182
16072:1.183
0.79710017
0.332-0044
20.248--7.00
0.584:00.022
16.550:2.495
0.812:6623
0.6501..172
19.422-9.141
14

---

3. Results on the Original Clean Visible and Infrared Image Fusion Task
We conduct the experiment fuse the original clean visible and infrared images. The experiment merely compares the fusion performances of Deno-IF and SOTA image fusion methods and verifies whether the proposed method will blur clean data. As shown in Figs. 9 our method preserve the content of two source images in more balanced manner, thus reflecting more scene contents in the fused image and assisting human visual perception with comparable contrast.
Visible
Infrured
TarDAL
CDDPase
DDFM
MetaPusion
PromptFusdon
(ours)
Figure Qualitative results on the LLVIP dataset when fusing clean visible and infrared images.
Visible
Infrared
TarDAL
CDDFese
DDFM
MetaFusion
FustonBosteer
PromptFFssin Fasion
DRMF
Text-IF
OmniFFus
Deno-1F (ours)
Figure 10: Qualitative results on the M3FD dataset when fusing clean visible and infrared images.
4. Quantitative Results of Detection under Noise Interference
Tab. reports the quantitative results of detection on the fusion results of noisy source images. The metrics include precision, recall, average precision (AP), and mean AP (mAP). mAP is the average of APs at IoU thresholds from 0.5 to 0.95 in steps of 0.05.
Table 6: Object detection results when source images suffer from noise.
Car
CT&CDD.
CT++DDEM
CT+
CT+Fusion.
CT++Propt..
DRMF
Text-IF
OmniFuse
Deno-IF
Precision
0.888
0.877
0.917
0.883
AP@0.5
0.763
0.787
08111
0.772
0.758
0.769
0.722
0.786
0.802
0.807
mAP
0.485
0.516
0.535
0.496
0.489
0.496
0.437
0.507
0.525
0.536
Validation of Convergence during Iterative Process
The first module is the convolutional low-rank module, which aims to decompose the noisy input into clean and noisy components. The decomposition problem is rewritten as the energy function in Eq. 45). To validate the convergence during the iterative process, we document several terms this energy function during iteration.
As summarized in Tab. 7 the iterative process low-rank optimization module demonstrates convergence. For M and N, they initialized rectangular identity-like matrices (all elements
15

---

on the main diagonal are and other elements are 0). As the initialized matrices do not contain much information, | and || initially increase incorporate more information, followed by a gradual stabilization as iteration progresses.
Table 7: Changes of various terms in the energy function during the iteration process.
Iterations
10
12
14
16
|R- L S
227.970
87,555
44.188
33.726
31.791
31.446
31,390
31,390
(L) MN]]
3821812
2395.634
1220.628
940.686
889.409
880.317
878.878
878.862
|
0.456
0.175
0.088
0.067
0.064
0,063
0.063
0.063
From the aspect of network optimization, clean components derived with convolutional low-rank priors serve i) physics-driven prior injector regularize fused images, and ii) implicit teachers that enable knowledge distillation of denoising priors into the fusion network. It enables simultaneous noise suppression and information fusion
Analysis of Computational Training Cost
As the training latency is dominated by the first stage (convolutional low-rank optimization module), we quantify the computational bottleneck within this module. In this module, computational com- plexity scales primarily with i) convolution kernel size (k1, k2) and ii) number of iterations (7") for solving the low-rank subproblem. The training costs of different settings are reported in Tab, [ It is observed that increasing k1, k2 incurs significant increase in training time while the number of iteration scales linearly with computational cost. Nevertheless, the entire training process remains practical, completing within several days.
Table 8: Changes of various terms in the energy function during the iteration process.
k l2
12
18
24
Training Time (m) / Epoch
41.31
54.35
71.51
93.28
121.22
151.68
T
10
20
30
40
50
60
Training Time (m) / Epoch
20.58
30.85
41.31
51.61
61.62
71.62
Differences with Prior Low-Rank Methods in Other Domains
While low-rank methods exist in some image processing (e.g., image smoothing, restoration, denois- ing), the proposed method differs from existing works in task formulation and low-rank theory.
For task formulation, the proposed method differs with prior low-rank methods in input complexity and objective shift. For input complexity, we handle multi-modal data rather than single-modal inputs. For objective, we focus on joint denoising and fusion rather than a single image processing task.
For low-rank theory, the proposed theory differs in energy function formulation and optimization strategy. For energy function, the regularization term is based on convolutional low-rank prior by the convolution nuclear norm. Compared with standard low-rank theory, it can avoid excessive smoothing and structural damage for better local structure preservation, noise robustness, and physical interpretability. For optimization strategy, previous researches directly implement the objective function as the loss of network. Differently, we design a two-phase optimization strategy. In the first phase, we rewrite the energy function and iteratively address the subproblems with analytical solutions. In the second phase, we design deep network to distill the physics-driven priors and realize denoising and fusion jointly.
Limitations
The convolutional low-rank optimization module serves as the theoretical cornerstone of the joint denoising and fusion network. It ensures physically-consistent guidance through iterative refinement during training but requires iterative optimization. Although it is not applied in the testing phase, it lengthens the overall training time. In future work, we plan investigate more efficient optimization schemes to preserve performance while reducing training costs.
16

---

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 abstract and introduction of the paper clearly state the claims made, including the contributions made in the paper and important assumptions and limitations.
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 the paper and important assumptions and limitations. A No or NA answer to this question will not be 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 long is clear that these goals are not attained by the paper.
2. Limitations
Question:
Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification:
The paper discusses the limitations of the work performed by the authors, providing a balanced view of the study's 's scope and potential areas for improvement.
Guidelines:
The answer NA means that the paper has limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (c.g.. independence assumptions, noiseless settings, model cell-specfficatinn asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g. if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution low or images are taken in low lighting. Or speech-to-tex, system might not be used reliably to provide closed captions for online lectures because it fails handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and faimesss.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community, Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
17

---

Answer: [Yes]
Justification: All the theorems, formulas, and proofs the paper have been numbered and cross-referenced, and all assumptions have been clearly stated referenced in the statement of any theorems.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch to provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proof's provided in appendix supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions the paper (regardless whether the code and data are provided or not)?
Answer: [Ys]]
The paper has fully disclosed all the information needed to reproduce the main experimental results of the paper the extent that affects the main claims and conclusions of the paper.
Justification:
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is novel architecture, describing the architecture fully might suffice, or if the contribution is specific model and empirical evaluation, it may be necessary to either make possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g.. in the case of large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurlPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.
(b) the contribution is primarily new model architecture, the paper should describe the architecture clearly and fully.
(c) the contribution is a new model (e.g., a large language model), then there should either be way access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (c.g., to registered users), but it should be possible for other researchers to have some path to reproducing verifying the results.
18

---

5. Open access to data and code
Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
Question:
Answer: [Yes]
Justification:
We will open access to the code after the paper is accepted.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurlPS code and data submission guidelines (https://nips. ec/
cublic/guidess htapli//guiiees/oeeeubbmmimsss
for more details.
While we encourage the release of code and data, we understand that this might be possible, so "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurlPS code and data submission guidelines (https
/nips.cc/publl cc/ http:publlcgggdeeee http:///oooeouooassssoooooooooee
for more details.
The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible supplemental material (appended the paper) is recommended, but including URLs to data and code is permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (c.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Ye]]
Justification: We have specified all the training and test details necessary understand the results.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, or as supplemental material.
7. Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [Yes]
Justification: The paper reports error bars and other appropriate information about the statistical significance of the experiments, ensuring the reliability and validity of the results.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by emror bars, confi- dence intervals, or statistical significance tests, least for the experiments that support the main claims of the paper.
19

---

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).
The method for caleulating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g., Normally distributed errors).
It should be clear whether the error bar is the standard deviation the standard error of the mean.
It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables figures symmetric error bars that would yield results that are out range (e.g. negative error rates).
If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments compute resources
Question:
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed reproduce the experiments?
Answer: [Yes]
The paper provides sufficient information on the computer resource for experi- ments.
Justification:
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs as well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g.. preliminary or failed experiments that didn't make into the paper).
9. Code of ethics
Question: Does the research conducted the paper conform, in every respect, with the NeurlPS Code of Ethics
Answer: [Yes]
Justification: The research conducted in the paper conforms in every respect with the NeurlPS Code of Ethics, ensuring ethical standards are upheld throughout the study.
Guidelines:
The answer NA
that the authors have reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make eration due to laws or regulations in their jurisdiction).
preserve anonymity (e.g., if there is special consid-
10. Broader impacts
Question: Does the paper discuss both potential positive socictal impacts and negative socictal impacts of the work performed?
Answer: [Yes]
Justification: The paper discusses both potential positive societal impacts and negative societal impacts of the work performed, providing comprehensive evaluation of the study's broader implications.
20

---

Guidelines:
The answer NA means that there is no societal impact of the work performed.
If the authors answer NA No, they should explain why their work has societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious unintended uses (c.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deploymemt of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology being used intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms monitor how system learns from feedback over time, improving the efficiency and accessibility ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [Yes]
The paper describes safeguards that have been put in place for the responsible release of data or models that have high risk for misuse, ensuring that appropriate measures are taken to mitigate potential risks.
Justification:
Guidelines:
The answer NA means that the paper poses such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Question: Are the creators or original owners of assets (c.g., code, data, models), used in the paper. properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
Justification:
The creators original owners of assets used in the paper are properly credited, and the license and terms of use are explicitly mentioned and properly respected.
Guidelines:
The answer NA means that the paper does not use existing assets.
Thh authors should cite the original paper that produced the code package dataset.
21

---

The authors should state which version of the asset is used and, if possible, include URL.
The name the license (e.g., CC-BY 4.0) should be included for each asset. For scraped data from a particular source (e.g.. website), the copyright and terms of service of that source should be provided.
If assets released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswi thoode com/ datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if has changed) should be provided. this information not available online, the authors are encouraged to reach out to the asset's s creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer:[Yes)
Justification:
The new assets introduced in the paper are well documented, and the docu- mentation is provided alongside the assets.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details the dataset/code/mmodl as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset used.
At submission time, remember anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification:
The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included in the main paper.
According to the NeurIPS Code of Ethies, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based the requirements of your country or institution) were obtained?
Answer: [NA]
Justification: The paper does not involve crowdsoureing nor research with human subjects.
Guidelines:
22

---

The answer NA means that the paper does not involve crowdsoureing nor research with
human subjects.
Depending on the country in which research is conducted, IRB approval (or equivalent)
may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and expect authors adhere the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Does the paper describe the usage of LLMs if it is important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Question:
Answer: [NA]
Justification: The core method development in the paper does not involve LLMs any important, original, or non-standard components.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer to LLM for what should or should not be described.
23


Reviews:
[{"review_id": "Z85vkzshEv", "reviewer": "Reviewer_8daB", "summary": "The paper proposes a two-stage framework for visible-infrared image fusion under noisy conditions. It integrates a convolutional low-rank optimization module for intra-modal denoising and a joint fusion network for cross-modal feature integration. By leveraging spatial alignment and analogical reasoning, the method enhances feature consistency and achieves superior performance across various noise levels.", "questions": "1.\tIn Figure 2, it is unclear whether the operator “c” represents feature concatenation, element-wise multiplication, or addition. This should be explicitly annotated in the figure.\n2.\tIn the fusion stage, it appears that the visible and infrared images are simply overlaid. How is noise suppression handled in this process?\n3.      In Subsection 3.3, the phrase “inter-modal recovery and intra-modal recovery and fusion” exhibits structural ambiguity, which may lead to misinterpretation. It is recommended that the authors revise the wording throughout the manuscript to enhance clarity and ensure the precise use of terminology.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\nThe proposed unsupervised image fusion framework introduces a novel perspective by jointly addressing denoising and fusion in noisy visible-infrared scenarios, which is a less explored yet practically important problem.\nThe integration of a convolutional low-rank optimization module enables effective extraction of clean components from noisy inputs without requiring paired training data, improving generalization across varying noise conditions.\nThe experimental section is comprehensive, covering diverse and variable noise settings, and convincingly demonstrates the effectiveness and robustness of the proposed method.\nWeaknesses\nSome aspects of the method, particularly the implementation and interplay between the convolutional low-rank optimization and the joint fusion network, are not clearly described and may hinder reproducibility.\nThe proposed method adopts a two-stage architecture, which, while modular in design, may lead to a more prolonged training process and reduced overall training efficiency. It is recommended to optimize the training strategy or provide an analysis of computational cost to enhance practical applicability.", "was_revised": false, "final_justification": "", "rebuttal": ">**Q1:  Implementation and interplay between the convolutional low-rank optimization and joint fusion network.**\n\n**A1**: We sincerely appreciate this constructive suggestion. We want to emphasize that the connection between these modules operates fundamentally via deep implicit physics-driven constraint propagation. The ultimate goal is to obtain a joint denoising and fusion module via an unsupervised approach, yet struggles to infer high-quality structural cues to guide network optimization.\n\nTo address it, the convolutional low-rank optimization module leverages convolutional low-rank priors to derive clean components. These components serve as i) physics-driven prior injector to regularize fused images, and ii) implicit teachers that enable knowledge distillation of denoising priors directly into the final joint denoising and fusion network. The design enables simultaneous noise suppression and information fusion while eliminating the need for low-rank module reuse during testing, thereby enhancing real-time inference efficiency.\n\nWe will emphasize the deep implicit connection and include a detailed algorithmic presentation in pseudocode format in the final version to enhance methodological clarity.\n\n>**Q2: Optimize the training strategy or provide an analysis of computational training cost.**\n\n**A2**: Thanks for your insightful suggestions. We first perform an analysis of computational cost. As the training latency is dominated by the first stage (convolutional low-rank optimization module), we quantify the computational bottleneck within this module. In this module, computational complexity scales primarily with i) convolution kernel size ($k_1,k_2$) and ii) number of iterations ($T$) for solving the low-rank subproblem. The training costs of different settings are reported below. We observe that increasing $k_1,k_2$ incurs significant increase in training time while the number of iteration scales linearly with computational cost. Nevertheless, the entire training process remains practical, completing within several days.\n\n|$k_1,k_2$|12|18|24|30|36|42|\n|--|--|--|--|--|--|--|\n|Training time/epoch (m)|41.31|54.35|71.51|93.28|121.22|151.68|\n\nWhen $k_1,k_2=12$, we change the number of iterations (T) and quantify the training cost. \n|$T$|10|20|30|40|50|60|\n|--|--|--|--|--|--|--|\n|Training time/epoch (m)|20.58|30.85|41.31|51.61|61.62|71.62|\n\nA possible solution to optimize the training strategy is to adaptively adjust $k_1$ and $k_2$ based on the scene content, and dynamically update $T$ according to the convergence behavior. We will report the training cost in the final version. In future work, we plan to further develop adaptive strategies for determining convolutional kernels and iterative updates to improve training efficiency.\n\n>**Q3: Annotation of operator “c” in Figure 2.**\n\n**A3**: Thanks for this question. The operator “c” represents feature concatenation. We will add the explicit annotation in Figure in the final version.\n\n>**Q4: How noise suppression is handled in the fusion stage.**\n\n**A4**: We appreciate this opportunity to clarify our fusion methodology. We want to clarify that the fusion process is not a simple overlay of source images, but rather a sophisticated extraction, recovery, fusion, and reconstruction process implemented through the joint denoising and fusion module.\n\nFrom the aspect of network architecture, noise is suppressed through Transformer blocks in both intra-modal recovery and inter-modal recovery and fusion.\n\nFrom the aspect of network optimization, clean components derived with convolutional low-rank priors serve as i) physics-driven prior injector to regularize fused images, and ii) implicit teachers that enable knowledge distillation of denoising priors into the fusion network. It enables simultaneous noise suppression and information fusion. In the final version, we will emphasize the implementation of denoising functionality in the network more prominently to prevent potential misunderstanding.\n\n>**Q5: Revise the phrase “inter-modal recovery and intra-modal recovery and fusion”.**\n\n**A5**: Thanks for your careful reading and reminder. The terms “inter-” and “intra-” are mistakenly swapped in the original manuscript. The correct terms should be \"intra-modal recovery\" and \"inter-modal recovery and fusion\". We will carefully revise the wording through the manuscript and ensure the precise use of terminology in the final version."}, {"review_id": "nngIQwCAUJ", "reviewer": "Reviewer_GvvZ", "summary": "This paper proposed an unsupervised method to fuse noisy infrared and visible images. The proposed method consists of two parts. The first part is a convolutional low-rank optimization module, which is used to generat clean images. The second part is a joint denoising and fusion module. The main advantage of the proposed method is that it is unsupervised. Additionally, a convolution matrix-based regularization loss is designed. A series of experiments on simulated noisy dataset are conducted to demonstrate the effectiveness of the proposed method.", "questions": "1. What is the motivation of this work? Based on the reviewer's experience, images collected using infrared an visible cameras are usually not noisy. Especially, the infrared images captured using a thermal infrared camera are clean.  The reviewer does not see the need of fusing noisy images, especially the manually-created noisy images. More convincing details should be provided to explain the motivcation. Otherwise, it seems that the authors created an issue (that may not exist in practise) and then proposed a method to solve this issue.\n\n2. Does the convolutional low-rank optimization module like a pre-processing module? The clean data generated by this module is then used as label for the supervision of the joint denosing and fusion module. Is it possible to replace this convolutional low-rank optimization module using an unsupervised denoising model, and then used the denoised data as supervision for the joint denosing and fusion module? What is the disadvantage of using an unsupervised image denoising module (compared to the proposed convolutional low-rank optimization module) except model size?\n\n3. In Fig. 2, it mentions \"inter-modal recovery\" and \"intra-modal recovery and fusion\". Should these be \"intra-model recovery\" and \"inter-model recovery and fusion\"?\n\n4. Why are the 5 image fusion metrics used in this paper selected out of tens of image fusion metrics in the literature? \n\n5. More recent methods, i.e., those published in late 2024 and 2025 should be compared.", "limitations": "Yes, limitations are discussed in the supplementary material.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- This paper is well-written and easy to follow\n- The joint denosing and fusion module is interesting\n- Extensive experimental results are provided\n- Image qualities are good\n\n\nWeaknesses:\n- The motivation of this work is not very clear. As mentioned in the Experiments part, there are no existing noisy infrared and visible datasets, thus the authors needed to simulate one. Additionally, there are many existing infrared and visible image datasets that contain \"clean\" images, indicating the infrared and visible images captured are generally not noisy. Given these, I am not sure why noisy infrared and visible image fusion is necessary. The need of creating noisy images manually and then develop a method to fuse noisy images is not clear. This motivation should be explained more clearly. Especially, the authors should explain very clearly that fusing noisy visible and infrared images is necessary and useful in practise. Otherwise, it is likely that this paper solves a \"fake\" issue.\n\n- It looks like that the proposed convolutional low-rank optimization module is a pre-processing module to generate clean data, which is then used as supervision. It is not clear why the authors do not use an unsuperivsed image denoising module. What is the disadvantage of using an unsupervised image denoising module (compared to the proposed convolutional low-rank optimization module) except model size? It seems that this kind of experiments and discussions are missing.\n\n- The compared methods are a bit outdated. Most of the compared methods were published before 2024. More recent methods, such as those published in 2024 and 2025 should be compared.\n\n- The selection of image fusion metrics are not justified. There are many available metrics, and a method can show different performance when different set of metrics are used. It is not clear why the authors selected the 5 metrics used in the paper. More justifications should be provided that the selected set of metrics can comprehensively evaluate image fusion methods.", "was_revised": true, "final_justification": "I have read the rebuttal and discussed with the authors. Thanks for the rebuttal, which is helpful for solving some of my concerns. Therefore, I changed my rating to a better level. \n\nHowever, I am still not fully convinced regarding the motivation and practical usage of this method. I think the proposed method is good as a publication, but not very useful in robots, autonomous driving, and many other practical applications. The authors can try to capture some RGB and thermal images using low-cost RGB-T or thermal cameras, and then test the proposed method.", "rebuttal": ">**Q1: Explain the necessity and motivation of fusing noisy infrared and visible images.**\n\n**A1**: We appreciate this constructive suggestion. We want to declare that the clean publicly available datasets do not reflect real-world acquisition. Methods should account for inherent noise from affordable hardware.\n\n1. Dataset quality does not reflect real-world feasibility\n\nExisting datasets are collected for the target of high image quality, thus relying on cost-prohibitive imaging systems to ensure high signal-to-noise ratios. For example, the platform of LLVIP is DS-2TD8166BJZFY-75H2F/V2, a binocular camera platform priced over $19,000. It not only has high-quality signal acquisition hardware devices, but also embeds dedicated and professional processing pipelines to enhance raw inputs, including adaptive AGC for dynamic range compression, DDE for nonlinear enhancement, and 3D DNR for denoising.\n\nHowever, in practice, most deployed multi-modal systems use affordable devices that may cost only several hundred dollars. The imaging systems may introduce critical distortions, such as degraded signal acquisition (low-cost microbolometers and CMOS suffer from more obvious noise), and ineffective embedded processing pipelines. These factors will result in a large amount of noise in the collected signal, which is not as high-quality as the dataset.\n\n2. Artificial bias\n\nExisting dataset is dedicated to providing benchmarks for evaluating fusion performance. As declared, the construction of dataset involves rigorous manual screening. Some initially captured samples with low quality maybe discarded. It creates a distributional gap: real-world images contain various noise absent in curated data.\n\nWe will add the discussion on necessity and motivation of fusing noisy images in the final version.\n\n\n>**Q2: Feasibility and disadvantages of replacing the convolutional low-rank optimization module with an unsupervised denoising model.**\n\n**A2**: We sincerely appreciate this insightful question, which crucially enhances the methodological rigor and expositional clarity of our work. The convolutional low-rank optimization module pre-inferences clean data as i) physics-driven prior injector to regularize fused images, and ii) implicit teachers that enable knowledge distillation of denoising priors. It seems like a pre-processing module but in fact, it is a core enabler of physics-aware and training-free supervision. \n\nIt is possible to replace it with an unsupervised denoising model. However, the substitution may introduce critical disadvantages:\n\n1. Theoretical grounding and interpretability\n\nConvolutional low-rank optimization is theoretically grounded in mathematical foundations and explicit priors, including matrix decomposition and low-rank representation theory. The objective function, constraints, and convergence of iterations are interpretable and theoretically guaranteed. Unsupervised denoising models rely on heuristic architectures with less rigorous justification for denoising mechanisms. Although certain priors can be embedded in network architecture, the success relies more on empirical design, preventing theoretical explanation and formal verification.\n\n2. Training-data dependency\n\nConvolutional low-rank optimization is training-free, operating directly on individual noisy inputs without the need of prior dataset. It is critical for unique or rare images lacking training samples. In contrast, while unsupervised denoising models avoid paired supervision, they still demand large training data, remaining fundamentally data-driven. The performance depends on training data distribution.\n\n3. Overfitting risks and generalization\n\nAs convolutional low-rank optimization is training-free to per image, it does not rely on learning dataset-specific patterns, exhibiting robustness to out-of-distribution cases and eliminating overfitting risks. Unsupervised denoising models employ fixed networks trained on finite data distributions. When encountering noise types or content underrepresented in training, the learned mappings may fail to generalize, leading to degradation.\n\nWe will add the discussion in the final version.\n\n>**Q3: Compare with more SOTA methods published in 2024 and 2025.**\n\n**A3**: Thanks for your helpful suggestion. To further strength the comparative analysis, besides comparing with Text-IF and DRMF published in 2024, we expand the competitors with three state-of-the-art methods published in 2025, including FusionBooster [r1], PromptFusion[r2] and OmniFuse [r3]. FusionBooster and PromptFusion are combined with CTNet for pre-denoising while OmniFuse is degradation-aware image fusion methods.\n\n[r1] FusionBooster: A Unified Image Fusion Boosting Paradigm. International Journal of Computer Vision, 2025.\n\n[r2] PromptFusion: Harmonized Semantic Prompt Learning for Infrared and Visible Image Fusion. IEEE/CAA Journal of Automatica Sinica, 2025.\n\n[r3] OmniFuse: Composite Degradation-Robust Image Fusion with Language-Driven Semantics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.\n\nAs qualitative comparison is not available, we report quantitative results under four conditions (different datasets with different types of noise) below. Our method achieves optimal performance on most metrics. Less optimal performances of CT.+FusionBooster and CT.+PromptFusion stem from the focus on addressing visual degradation caused by distortions of critical features and details, prioritizing texture preservation. However, CTNet's limited denoising capability results in residual noise erroneously amplified in fusion results. The comparatively lower results of OmniFuse stem from the unified model for multiple types of degradations and supervised paradigm.\n\n|Gaussian|||LLVIP|||||M3FD|||\n|-|-|-|-|-|-|-|-|-|-|-|\n|Metrics|SSIM$\\uparrow$|PSNR$\\uparrow$|FSIM$\\uparrow$|CC$\\uparrow$|BRISQUE$\\downarrow$|SSIM$\\uparrow$|PSNR$\\uparrow$|FSIM$\\uparrow$|CC$\\uparrow$|BRISQUE$\\downarrow$|\n|CT.+FusionBooster|0.270|11.694|0.760|0.679|$\\underline{26.381}$|0.459|10.618|0.744|$\\underline{0.565}$|**16.474**|\n|CT.+PromptFusion|$\\underline{0.433}$|$\\underline{15.811}$|$\\underline{0.803}$|$\\underline{0.702}$|36.876|0.474|$\\underline{15.007}$|$\\underline{0.777}$|0.470|25.837|\n|OmniFuse|0.377|12.642|0.731|0.642|41.046|$\\underline{0.540}$|14.488|0.763|0.459|46.787|\n|Deno-IF (ours)|**0.616**|**17.070**|**0.811**|**0.738**|**16.725**|**0.585**|**15.637**|**0.788**|**0.563**|$\\underline{24.298}$|\n\n|Speckle|||LLVIP|||||M3FD|||\n|-|-|-|-|-|-|-|-|-|-|-|\n|Metrics|SSIM$\\uparrow$|PSNR$\\uparrow$|FSIM$\\uparrow$|CC$\\uparrow$|BRISQUE$\\downarrow$|SSIM$\\uparrow$|PSNR$\\uparrow$|FSIM$\\uparrow$|CC$\\uparrow$|BRISQUE$\\downarrow$|\n|CT.+FusionBooster|0.338|11.909|0.737|$\\underline{0.683}$|$\\underline{29.446}$|0.516|10.950|0.774|$\\underline{0.612}$|$\\underline{21.178}$|\n|CT.+PromptFusion|0.381|$\\underline{15.212}$|$\\underline{0.787}$|0.681|48.286|0.522|$\\underline{15.881}$|$\\underline{0.784}$|0.521|21.837|\n|OmniFuse|$\\underline{0.479}$|12.859|0.741|0.665|39.696|$\\underline{0.585}$|15.221|0.767|0.523|44.850|\n|Deno-IF (ours)|**0.633**|**16.528**|**0.805**|**0.738**|**9.280**|**0.656**|**16.525**|**0.813**|**0.648**|**16.514**|\n\nThe computational complexity and efficiency of these SOTA methods and the proposed Deno-IF are reported below. Our method achieves lower computational complexity and higher inference efficiency.\n\n|Complexity|FusionBooster|PromptFusion|CT.+FusionBooster|CT.+PromptFusion|OmniFuse|Deno-IF (ours)|\n|-|-|-|-|-|-|-|\n|Para (m)|0.56|7.44|$\\underline{51.97}$|58.91|98.15|**1.43**|\n|GFLOPs|241|36877|41845|78481|$\\underline{5471}$|**115**|\n|Runtime (s)|2.49|1.08|15.96|14.55|$\\underline{4.53}$|**0.06**|\n\nWe will add the results with [r1]-[r3] in the final version to improve the previous comparison.\n\n>**Q4: Reason of selecting the five evaluation metrics.**\n\n**A4**: Thanks this question. Traditional fusion metrics can be categorized into similarity-based metrics (evaluating similarity between fused and clean source images) and statistics-based metrics (measuring fused image characteristics, such as SD, SF, MG, EN, etc.). However, when fusing noisy images, statistics-based metrics may produce misleading results as noise residuals can artificially inflate values (e.g., higher EN/SD/SF caused by noise rather than true useful information). We avoid this issue from two aspects.\n\n1. Similarity-based metrics\n\nWe prioritize similarity-based metrics that explicitly measure information fidelity over statistical properties to ensure reliable and robust evaluation. The selected metrics, i.e., PSNR, SSIM, CC and FSIM, provide complementary assessments of: i) absolute intensity preservation, ii) structural and texture similarity, iii) global feature distribution consistency, and iv) frequency-domain characteristic retention. The orthogonal combination of these multi-dimensional metrics offers a comprehensive evaluation of information fidelity.\n\n2. No-reference quality-based metrics \n\nTo assess image quality, we employ BRISQUE instead of conventional statistics-based metrics. By analyzing natural scene statistics through normalized pixel intensity distributions, BRISQUE detects unnatural distortions via mean-subtracted contrast-normalized coefficients and generalized Gaussian modeling. Its trained regression model penalizes noise-induced deviations from natural image statistics, yielding scores consistent with human perception and reliable for noisy scenarios.\n\n>**Q5: Correct “inter-modal recovery” and “intra-modal recovery and fusion” in Fig. 2.**\n\n**A5**: Thanks for careful reading and catching the terminology inconsistency. The correct terms should be \"intra-modal recovery\" and \"inter-modal recovery and fusion\". The terms “inter-” and “intra-” are mistakenly swapped while “modal” is correct as it refers to distinct imaging modalities rather than “model”. We will carefully correct all instances in the final version."}, {"review_id": "0YqPshEaP2", "reviewer": "Reviewer_oJaz", "summary": "This paper proposes Deno-IF, an unsupervised method for fusing noisy visible-infrared images that simultaneously addresses denoising and fusion without requiring clean training data. The key innovation lies in two synergistic modules: (1) A convolutional low-rank optimization module that decomposes noisy inputs into clean components through nuclear norm minimization, providing physics-guided training signals; and (2) I2Former, a transformer network combining intra-modal recovery (self-attention denoising) and inter-modal fusion (cross-attention integration). Extensive experiments on M3FD and LLVIP demonstrate that the proposed method exhibits advantageous capabilities in denoising and aggregating complementary information, along with strong generalization.", "questions": "1. According to the parameters of most comparison methods, the parameter of CTNet is about 50M, while the parameter of SwinFusion+CTNet is 5.38M.\n\n2. In the joint denoising and fusion module, there are many transformer blocks either for inter-modal recovery or intra-modal recovery and fusion. The features of these blocks are different. Can Eqs. (10)-(11) be distinguished based on different blocks?\n\n3. Why is CTNet chosen as the comparison method for denoising source images instead of other methods?", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "## Strengths\n\n1. This manuscript proposes an unsupervised image fusion network for noisy infrared and visible images, which fully exploits the low-rank characteristics of clean data. \n\n2. By integrating the proposed convolutional low-rank optimization module, the authors design a lightweight joint denoising and fusion module that maintains strong denoising and information integration capabilities while ensuring high computational efficiency.\n\n3. Extensive comparative experiments, ablation studies, downstream task evaluations, and efficiency analyses validate the effectiveness of the proposed approach.\n\n4. Targeting the noise issues commonly encountered in real-world infrared and visible image fusion systems, the proposed method contributes to broadening the application scope of such fusion tasks. Moreover, the paper declares clear motivation and well-organized writing, which is easy to follow and understand.\n\n## Weaknesses\n\n1. The Convolutional Low-Rank Optimization Module and the Joint Denoising and Fusion Module appear somewhat decoupled. Emphasizing their connection would help readers better understand the proposed framework.\n\n2. The related work discusses existing image fusion methods. Highlighting how this method differs from prior low-rank methods in other domains will further improve the contribution of this work.\n\n3. In Figure 1, the noise in the visible image is not visually obvious. Replacing it with a scene where the noise is more clearly noticeable could better highlight the value of the proposed method. Besides, some details need to be corrected. For example, an extra “.” appears on line 60, and the acronym MAP on line 128 should be explicitly explained.", "was_revised": true, "final_justification": "The response effectively addressed my concerns, and I have decided to maintain my accept score.", "rebuttal": ">**Q1: Connection between two modules.**\n\n**A1**: We sincerely appreciate this for this constructive suggestion. We want to emphasize that the connection between these modules operates not through superficial explicit-level structural transfer of data or feature, but fundamentally via deep implicit physics-driven constraint propagation. The ultimate goal is to obtain a joint denoising and fusion module via an unsupervised approach, yet struggles to infer high-quality structural cues to guide network optimization. To address it, the convolutional low-rank optimization module leverages convolutional low-rank priors to derive clean components. These components serve as i) physics-driven prior injector to regularize fused images, and ii) implicit teachers that enable knowledge distillation of denoising priors directly into the final joint denoising and fusion network. The design enables simultaneous noise suppression and information fusion while eliminating the need for low-rank module reuse during testing, thereby enhancing real-time inference efficiency. We will emphasize the deep implicit connection in the final version.\n\n>**Q2: Differences with prior low-rank methods in other domains.**\n\n**A2**: While low-rank methods exist in image processing (e.g., image smoothing, image restoration, image denoising), the proposed method differs from existing works in both task formulation and low-rank theory:\n\n1. Task formulation\n\nThe proposed method differs in input complexity and objective shift. For input complexity, we handle multi-modal data rather than single-modal inputs. For objective, we focus on joint denoising and fusion rather than a single image processing task.\n\n2. Low-rank theory\n\nThe proposed theory differs in energy function formulation and optimization strategy. For energy function, the regularization term is based on convolutional low-rank prior by the convolution nuclear norm. Compared with standard low-rank theory, it can avoid excessive smoothing and structural damage for better local structure preservation, noise robustness, and physical interpretability. For optimization strategy, previous researches directly implement the objective function as the loss of network. Differently, we design a two-phase optimization strategy. In the first phase, we rewrite the energy function and iteratively address the subproblems with analytical solutions. In the second phase, we design a deep network to distill the physics-driven priors and realize denoising and fusion jointly.\n\nWe will highlight the contributions of the proposed method over existing low-rank methods in other domains in the final version.\n\n>**Q3: Replace Fig. 1 with more noticeable noise.**\n\n**A3**: We appreciate this insightful recommendation. We will replace the qualitative examples in Fig.1 with the comparison examples where both the visible and infrared images suffer from more clearly noticeable noise in the final version. Compared with the combination of SOTA image denoising and fusion methods, the proposed method can restore and represent essential high-quality structural information from source images with greater clarity in the fusion result.\n\n>**Q4: Annotate the block number in equations.**\n\n**A4**: Thanks for this suggestion. In both intra-modal recovery and inter-modal recovery and fusion parts, there are many blocks. To avoid the confusion between the block numbers of these two parts, we discuss them separately. In Eq. (10) for intra-modal recovery, the output of the current block (e.g., $\\hat{\\textbf{f}}_v$) is only related to the input of the current block (e.g., $\\textbf{f}_v$). It does not involve complex update process as Eqs. (6a)-(6d). For the sake of formula simplicity, the block numbers will not be given. In Eq. (11) for inter-modal recovery and fusion, the input of each block includes visible, infrared and even earlier fused features. However, the directions of intra-modal recovery and inter-modal recovery and fusion are different. The earlier blocks of inter-modal recovery and fusion need to fuse the features of the later blocks of intra-modal recovery. This is clearly represented in Fig. 2, so the block number will not be labeled to avoid confusion.\n\n\n>**Q5: Reason of selecting CTNet for comparison.** \n\n**A5**: The rationale of selecting CTNet for comparison is two-fold: generalization validation between unsupervised and supervised approaches, and architecturally relevant comparison.\n\n1. Validating unsupervised generalization against supervised performance\n\nCTNet represents a SOTA supervised denoising approach, requiring paired clean and noisy images for training. Our method is an unsupervised denoising and fusion framework, leveraging the convolutional low-rank prior without needing clean data. Including CTNet for comparison serves a critical purpose: testing the generalization and denoising effectiveness of our unsupervised approach. Comparing against a top-performing supervised denoising method, our unsupervised method achieves competitive or even superior denoising performance. It validates the robustness and applicability of our approach, especially when paired training data is unavailable or difficult to obtain.\n\n2. Architecturally relevant comparison\n\nCTNet and our method are fundamentally based on Transformer. By comparing with CTNet, it provides a controlled comparison point, specifically highlighting improvements of divergent frameworks and objectives. CTNet utilizes Transformer primarily to mine structural information and explore multi-level interactions for extra information, all within a single modality for denoising. In contrast, our method uses Transformer for both multi-modal image denoising and fusion. It simultaneously addresses intra-modal recovery and inter-modal recovery and fusion within a unified framework. Comparing against CTNet, a typical representative of Transformer-based denoisers, allows to directly highlight the effectiveness and novelty of our multi-modal denoising- and fusion-oriented Transformer design.\n\nWe will provide a brief introduction to CTNet and the reason for leveraging it for comparison in the final version.\n\n>**Q6: Typo and description.**\n\n**A6**: Thanks for pointing out the issues related to typo and detailed description. The parameter of SwinFusion+CTNet is 52.38M. The circle size in Fig.1 represents the correct parameter number. MAP represents classic Maximum A Posteriori inference to integrate physics-driven priors with data-driven fitting for robust decomposition. We will carefully correct the typos (extra “.” and parameter) and description in the final version and further correct the presentation of figures and tables."}, {"review_id": "tNdY6inJv6", "reviewer": "Reviewer_XjA5", "summary": "This paper explores how noise degradation affects image fusion in real-world imaging scenarios and introduces Deno-IF, an unsupervised method for fusing noisy visible and infrared images. Leveraging the convolutional low-rank priors of clean images, it first develops a convolutional low-rank optimization module to extract clean image components from noisy inputs. Following this, a specially designed network, combining inter-modal recovery and intra-modal processing, performs denoising and fusion simultaneously. As a result, the proposed method achieves effective noise-suppressed image fusion with fewer network parameters. Extensive experiments are conducted under various noise types, intensities, and presence conditions to assess the method’s effectiveness and generalization capability.", "questions": "i) To assess robustness, the experiments involve various levels of Gaussian noise. However, it is unclear whether this noise is added to the visible image, the infrared image, or both.\n\nii) The parameters in the convolutional low-rank optimization module are determined using manually designed operations aimed at estimating the noise level. What is the rationale behind this choice?\n\niii) The linear mapping $A_k(\\cdot)$ transforms an image of size $h \\times w$ into a convolutional matrix of size $hw \\times k_1k_2$. Has there been any investigation into whether increasing $k_1$ and $k_2$ yields improvements in performance?\n\niv) The final loss function includes several hyperparameters. While most are kept constant during training, $\\kappa$ is gradually increased. Why?", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\ni) The work addresses the challenge of noisy data in real-world conditions by proposing a unified framework that integrates denoising and fusion, making it suitable for a broader range of practical applications.\n\nii) The proposed approach is fully unsupervised—it eliminates the need for paired noisy and clean image data by automatically estimating clean components using convolutional low-rank decomposition.\n\niii) The method not only applies the convolutional low-rank prior in the decomposition stage but also introduces it as part of the loss function to enhance the quality of the fused image. This represents a novel loss formulation in the context of image fusion.\n\niv)The paper presents its methodology with clear structure, and the experimental evaluations are thorough—covering diverse noise types and levels—which demonstrates both the effectiveness and strong generalization ability of the proposed method.\n\nWeaknesses\n\ni) The training process of the proposed method is both complex and computationally intensive. This is largely due to the iterative nature of the convolutional low-rank optimization, which is further burdened by the need to train the denoising and fusion components of the network jointly.\n\nii) The paper does not sufficiently explain how the no-reference metric BRISQUE is used to evaluate the quality of the fused images. Additionally, the results of BRISQUE assessments under various noise levels should be included to enhance the evaluation.\n\niii) There are several typographical issues that need correction: (a) In the abstract, the phrase “intra-modal recovery and intra-modal recovery and fusion” should be revised to “intra-modal recovery and inter-modal recovery and fusion.” (b) The expression “Experiments on different-level noise Gaussian noise” is redundant and unclear, and should be corrected for clarity.", "was_revised": true, "final_justification": "Thank the authors for their efforts in clarifying these points. As a result, the issues have been addressed, and I will maintain my rating.", "rebuttal": ">**Q1: Training complicacy and computation complexity.**\n\n**A1**: We appreciate this insightful observation. To enhance methodological clarity, the final version will include a detailed algorithmic presentation in pseudocode format.\n\nBesides, we want to clarify the computational efficiency in the testing phase. While the iterative optimization in the convolutional low-rank module increases computational overhead, the complexity is confined to the training phase. This module serves solely as an implicit knowledge distillation mechanism to guide the joint denoising and fusion network. During inference, it is completely bypassed, not incurring additional computation. The computational efficiency comparison results in Tab. 3 in the submitted manuscript also indicate that our method achieves optimal efficiency (including parameter numbers, FLOPs, and inference time) during the testing phase.\n\n>**Q2: No-reference metric BRISQUE and no-reference results under different-level noise.**\n\n**A2**: BRISQUE measures image quality by quantifying deviations from natural scene statistics in spatial domain. It extracts spatial features from the empirical distribution of locally normalized luminance and products of locally normalized luminance under a spatial natural scene statistic model. The features capture losses in naturalness caused by distortions, generating a holistic measure of quality. The no-reference comparison results under multi-level noise are reported in the following table. Key observations are analyzed from two perspectives:\n\n|$\\sigma$|10|20|30|40|50|\n|--|--|--|--|--|--|\n|DRMF|26.855|58.717|66.016|74.548|75.814|\n|Text-IF|33.060|45.843|58.087|61.441|66.258|\n|Deno-IF|15.364|16.493|17.989|20.514|24.256|\n\n1. Robustness to increasing noise severity\n\nAs noise level escalates, comparison degradation-aware image fusion methods exhibit significant performance degradation, while our method maintains stable quality preservation. It demonstrates our method’s inherent robustness to multi-level noise interference.\n\n2. Effectiveness per noise level\n\nAt each noise level, our method achieves best-in-class BRISQUE scores, outperforming comparison degradation-aware image fusion methods. The consistently low BRISQUE scores confirm our method's robustness in joint denoising-fusion, effectively maintaining naturalness under noise interference.\n\nWe will add the introduction and selection reasons of BRISQUE and other metrics and provide the above results in the final version.\n\n>**Q3: Clarification on multi-level Gaussian noise.**\n\n**A3**: Thanks for the query about multi-level Gaussian noise targets. To validate the robustness, we perform the experiment where both the visible and infrared images suffer from gaussian of different-level Gaussian noise. We will provide qualitative results of multi-level noise in the final version, which demonstrates our method’s robustness to multi-level noise interference.\n\n>**Q4: Parameter settings in the convolutional low-rank optimization module.**\n\n**A4**: These settings stem from principled trade-offs and computational efficiency:\n\n1. Principled trade-off\n\nThese parameters critically balance three competing objectives, including data fidelity, regularization, and releasing terms. The parameter settings directly correlate with the characteristics of source images, particularly noise levels. Higher noise level demands stronger regularization to suppress noise while lower noise level requires weaker regularization to preserve details. \n\n2. Computational efficiency imperative\n\nWhile deep learning-based noise estimation networks exist, such approaches incur prohibitive computational overhead, especially when the training process is already time-consuming. The manually designed estimator can achieve optimal efficiency-accuracy trade-off.\n\nConsidering these two aspects, a compromise is made by using manual operations. We will add these reasons of manually designed operations for parameter settings in the final version.\n\n>**Q5: Does increasing $k_1,k_2$ lead to performance improvements?**\n\n**A5**: Theoretically, increasing the size of convolutional kernel can contain more structural information for inference. However, excessively large convolutional kernels constrain the convolutional low-rank nature of more complex scenes, which may potentially comprise the preservation of complex information.\n\nIn terms of experimental verification, we increase $k_1,k_2$ and retrain the network. As empirically validated in the table below, appropriately increasing $k_1,k_2$ improves the performance. When $k_1,k_2$ may be excessively (due to time and equipment limitations, we increase $k_1,k_2$ to 36 for feasible experiments to observe trends), enforcing convolutional low-rank constraints over largely expansive regions risks damaging local details. It is reflected in a slightly decreasing performance trend.\n\n|$k_1,k_2$|12|18|30|36|\n|--|--|--|--|--|\n|Training time/epoch (m)|**41.31**|$\\underline{54.35}$|93.28|121.22|\n|SSIM$\\uparrow$|0.616|0.617|$\\underline{0.640}$|**0.643**|\n|PSNR$\\uparrow$|17.070|17.122|**17.286**|$\\underline{17.239}$|\n|FSIM$\\uparrow$|0.811|0.814|$\\underline{0.818}$|**0.819**|\n|CC$\\uparrow$|0.738|0.733|**0.737**|$\\underline{0.736}$|\n|BRISQUE$\\downarrow$|16.725|$\\underline{16.633}$|**16.155**|18.713|\n\nBesides, computationally, increasing kernel sizes $\\{k_1,k_2\\}$ generates a convolutional matrix of dimensions $ hw\\times{k_1k_2}$, leading to prohibitive computational costs.\n\nThanks again for this question which provides more validation and optimized settings of $k_1,k_2$. We will add the experiments about different settings of $k_1,k_2$  in the final version.\n\n\n\n>**Q6: Progressively increasing setting of $\\kappa$.**\n\n**A6**: The dynamically increasing setting of $\\kappa$ is motivated by the need to balance competing objectives: data fidelity (similarity losses between source and fused data) and regularization (convolutional low rankness of fused images). Through extensive experiment, we observe that: i) Large initial $\\kappa$ forces the network to over-prioritize the low-rank constraint, causing fused image to be excessively simplified (overly low-rank). It suppresses critical structural details in source images, leading to serious information distortion. ii) Small initial $\\kappa$ allows data fidelity to dominate early training, progressively incorporating richer information into fused image. However, a fixed small $\\kappa$ later renders the regularization ineffective, failing to refine high-frequency details. The scheduled increasing strategy of $\\kappa$ prioritizes data fidelity to preserve scene content in the early stage and gradually strengthen the low-rank prior to refine textures in the later stage.\n\n>**Q7: Typographical issues to be corrected.**\n\n**A7**: Thanks for point out the typos. We will carefully correct them as “intra-modal recovery and inter-modal recovery and fusion” and “different-level Gaussian noise” in the final version and further improve the description."}]

Author rebuttal:

Meta-review: This paper presents Deno-IF, an unsupervised method for fusing noisy visible and infrared images. The core idea is to jointly perform denoising and fusion. This is achieved through two main components: a convolutional low-rank optimization module that extracts clean image components from noisy inputs, and a unified network that then performs the fusion.

The strengths of the paper lie in its novel unsupervised approach to a practical problem, addressing noise in real-world fusion scenarios without needing paired clean data. The framework is technically sound, and the use of a convolutional low-rank prior as a loss function is an interesting contribution. The experimental validation is thorough and demonstrates the method's effectiveness.

Initial reviews raised concerns about the motivation (questioning the prevalence of noisy real-world data), the clarity of certain methodological details, and the selection of comparison methods. The authors provided a comprehensive rebuttal, successfully clarifying the real-world necessity of handling noise from affordable sensors, explaining the interplay between modules, and adding comparisons with more recent state-of-the-art methods. These responses satisfied the reviewers, leading to a consensus for acceptance.
Decision: Accept (spotlight)