ControlFusion: A Controllable Image Fusion Network with Language-Vision Degradation Prompts
Authors: Linfeng Tang
Yeda Wang
Zhanchuan Cai
Junjun Jiang
Jiayi Ma
Venue: NeurIPS 2025 oral ()
Keywords: Image fusion
multimodal images
degradation
Primary area: applications

Abstract: Current image fusion methods struggle with real-world composite degradations and lack the flexibility to accommodate user-specific needs. To address this, we propose ControlFusion, a controllable fusion network guided by language-vision prompts that adaptively mitigates composite degradations. On the one hand, we construct a degraded imaging model based on physical mechanisms, such as the Retinex theory and atmospheric scattering principle, to simulate composite degradations and provide a data foundation for addressing realistic degradations. On the other hand, we devise a prompt-modulated restoration and fusion network that dynamically enhances features according to degradation prompts, enabling adaptability to varying degradation levels. To support user-specific preferences in visual quality, a text encoder is incorporated to embed user-defined degradation types and levels as degradation prompts. Moreover, a spatial-frequency collaborative visual adapter is designed to autonomously perceive degradations from source images, thereby reducing complete reliance on user instructions. Extensive experiments demonstrate that ControlFusion outperforms SOTA fusion methods in fusion quality and degradation handling, particularly under real-world and compound degradations.

ControlFusion: A Controllable Image Fusion Network with Language- Vision Degradation Prompts
Linfeng Ta Yeda Wang Zhanchuan Cai?, Junjun Jiang', Jiayi Ma
Electronic Information School, Wuhan University Sccooo of Computer Science and Engineering, Macau University of Science and Technology Faaultyy of Computing, Harbin Institute of Technology 1infeng04190gmal com, wangyeda@whu edu. en, zccai0must www.0muuuuuuuutooommmommmm ed, jiangjung jun
jangjunjun@hit un@hit edu. cn, jyma20100gmail com,
Abstract
Current image fusion methods struggle with real-world composite degradations and lack the flexibility accommodate user-specific needs. To address this, we propose ControlFusion, a controllable fusion network guided by language-vision prompts that adaptively mitigates composite degradations. On the hand, we construct a degraded imaging model based on physical mechanisms, such as the Retinex the- ory and atmospheric scattering principle, to simulate composite degradations and provide a data foundation for addressing realistic degradations. On the other hand, we devise a prompt-move modulated restoration and fusion network that dynamically en- hances features according to degradation prompts, enabling adaptability to varying degradation levels. support user-specific preferences in visual quality, a text en- coder is incorporated to emhed user-defined degradation types and levels as degrada- tion prompts. Moreover, a ppattal-frrquency collaborative visual adapter is designed to autonomously perceive degradations from source images, thereby reducing com- plete reliance on user instructions. Extensive experiments demonstrate that Control- Fusion outperforms SOTA fusion methods in fusion quality and degradation han- dling, particularly under real-world and compound degradations. The source code is publicly available at httht:ss /ttp::///gthtbt cwwm Linffnnffffnngg SaaaffengTang///0 Lang/ControlFusion
1
Introduction
Image fusion is a crucial technique in image processing. By effectively leveraging complementary information from multiple sources, the limitations associated with information collection by single- modal sensors can be significantly mitigated [4]]. Among the research areas in this field, infrared- visible image fusion (IVIF) has garnered considerable attention. IVIF integrates essential thermal information from infrared (IR) images with the intricate texture details from visible (VI) images, offering a richer and more complete depiction of the scene [46]. By harmonizing diverse information and producing visually striking results, IVIF has found extensive applications in areas such security surveillance [47, military detection (22] scene understanding (44), and assisted driving (, etc.
Recently, IVIF has emerged as focal point of research, leading to rapid advancements in related methods. Based on the adopted network architectures, these methods can be categorized into convolutional neural network-
[29] 51], autoencoder-basse
[10] [1]. generative adversarial
network-basee [9 15). Transformer-basee [20 45], and diffusion model-based [50] 32 methods.
In addition, from functional perspective, they can be grouped into visual-oriented
29 51, joint
registration-fusicn 27 37,. semantic-driven 28 16]. and degradation
31 schemes.
Equal contribution;
Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Roal-world single degracation scenario
(wi Degree sensitiiit analysis
GLIM-DA
[s| Vaside
(b Inflardd
60 TeatiF 20)
Composita degradation scenario
Ja Vaitbee
Intrared
16) TediF (CVPR 243
[d] Ours
Pranpe Peange anfered
images han gradky Bllvel towwlight protiees
Figure I: Comparison across real-world, composite degradation, and varying degradation levels.
Notably, although degradation-robuss schemes can mitigate degradation to some extent, certain limitations exist. First, existing restoration-fusicn methods often adopt simplistic strategies for training data construction, overlooking the domain gap between simulated data and realistic images, which hampers their generalizability in practical scenarios, as illustrated in Fig. ()) Second, they tailored for specific single types of degradation, making them ineffective in handling more complex composite degradations, as illustrated in Fig. (II). Finally, as shown in Fig. (III, existing methods lack degradation level modeling, causing sharp decline in performance degradation intensifies. Moreover, they lack flexibility to adapt fusion results to diverse user preferences.
To overcome these limitations, we propose versatile and controllable fusion model based on language-vision prompts, termed ControlFusion. On the one hand, we introduced a physics- driven degradation imaging model that differs from existing approaches by simultaneously simulating degradation processes for both visible and infrared modalities with high precision. This model not only effectively narrows the gap between synthetic data and real-world images but also provides crucial data support for addressing complex multi-modal composite degradation challenges. On the other hand, we develop prompt-modulated image restoration and fusion network to generate high-quality fusion results. The prompt-modulated module enables our network to dynamically adjust feature distribution based on degradation characteristics (which can be specified by users), achieving robust feature enhancement. This also allows our method to respond to diverse user requirements. Furthermore, we devise a oppatall-frequnccy visual adapter that combines frequency- domain degradation priors to directly extract degradation cues from degraded inputs, eliminating the heavy reliance on user instructions, as customizing prompts for each scene is time-conguning and labor-intensive. As shown in Fig. benefiting from the aforementioned designs, our method excels in handling both real-world and composite degradation scenarios and can flexibly respond to user needs. In summary, our main contributions are as follows:
We propose CostrolFusion, versatile image restoration and fusion framework that uniformly models diverse degradation types and degrees, using textual and visual prompts as a medium. Specifically, its controllability enables it respond user-specific customization needs.
A frreuuncylfrruunyyyyc visual adapter is devised to integrate frequency characteristics and directly extract text-aligned degradation prompts from visual images, enabling automated deployment. A physics-driven imaging model is developed. integrating physical mechanisms such as the Retinex theory and atmospheric scattering principle to bridge the gap between synthetic data and real- world images, while taking into account the degradation simulation of fffraredvvsiblle dual modalities.
2
Related Work
Image Fusion. Earlier visual-oriented fusion approaches primarily concentrated on merging comple- mentary information from multiple modalities and improving visual fidelity. The mainstream network architectures primarily include CNN--bsed [44 48 AE-based 49), GaN-based [19 15), Trans- formers 2 and diffusion models [50] Furthermore, several schemes including joint registration and methods, are proposed to broaden the practical applications of image fusion. For instance, Tang et al. (30 and Liu et al. [17] developed corresponding solutions for illumination distortions and noise interference, respectively. In addition,
fusion 33 37 semantic-driven 15), and degradation- 39 42
[39] proposed a image restoration and fusion network to address various degradations.

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However, it fails to handle mixed degradations and struggles to generalize to real-world scenarios. In particular, it relies on tedious manual efforts to customize text prompts for each scene, hindering large-scale automated meployment.
Image Restoration with Vision-Language Models. With the advancement of deep learning toward multimodal integration, the image restoration field has progressively evolved into new paradigm of text-driven schemes. CLIP [25] establishes visual-textual semantic representation alignment through dual-stream Transformer encoders pre-trained on 400 million image-text pairs. This framework lays the foundation for the widespread adoption of Prompt Engineering computer vision. Recent studies integrate CLIP encoders with textual user instructions, proposing generalized restoration frameworks including PromptIR [23], AIRoDIR
[8] and InstructIR (2], which effectively handle diverse degradation types. Moreover, SPIRE 24 further introduces finegrraine textual restoration cues by quantifying degradation severity levels and supplementing semantic information for precision restoration. To eliminate reliance on manual guidance, Luo et al. [18] developed DA-CLIP by fine-tuning CLIP on mixed-degradation datasets, enabling degradation-an distortion handling. However, existing unified restoration methods are primarily designed for natural images, which exhibit notable limitations when processing multimodal images (e.g., infrared-visible).
embeddings facilitate
3 Physics-driven Degraded Imaging Model
Due to the differences in the imaging mechanisms of infrared and visible, the types of degradation they face also vary. To tackle the complex and variable degradation challenges, we propose a physics-driven imaging model for infrared and visible images aimed at reducing the domain gap between simulated data and real-world imagery. Infrared (IR) images usually suffer from sensor- related interference, such stripe noise and low contrast. While Visible (VI) images are typically degraded by illumination conditions (low light, over-exposure), weather (rain, haze), and sensor- related issues (noise, blur). Given a clear image I,, (m E {ir, vi the proposed imaging model can be mathematically represented as:
Dm = (Pw (Pi (4m))),
(I)
where Dm the corresponding degraded image, and P,, P and P. represent illumination-, weather-, and sensor-related distortions, respectively.
Sensor-related Distortions. Sensor-related distortions encompass various types of noise, motion blur, and contrast degradation. Contrast degradation and stripe noise of infrared images can be modeled as:
D = (T,).) J lgn
where a is a constant less than for contrast reduction, 1 E RRd is an all-ones column vector and I2 represents the column-wise Gaussian noise vector sampled from A/ e²) with E [1, 15]. Moreover, Gaussian noise and motion blur in source images can be modeled as:
D P.(... Im ((N + o0)
(3)
where N/0 x2) represents Gaussian noise with a E 5, 20). * denotes convolution with blur kernel K (N, 0) = + Ro (8, @ IN) constructed by rotating an impulse 6, @ 1N with random angle Rg (0 E [10, 80)), using N to normalize the kernel energy. Here, N E [3, 12] controls the blur level.
Illuiination-rlltted Distortions. Following the theoretical framework of Retinex, the formation of dllumieation-degraeed images Di is mathematically modeled as:
D'r P:(loz) 4 LT,
where L is the illumination map estimated by LIME [5]. 7 E [0.5,3] controls the illumination level.
Weather-related Distortions. According to the methodologies in
we employ the following formula to simulate weather-related degradations (i.e. rain and haze):
D = Par (1ms) = lui + A(1 - t) + Rt
(5)
where t and A denote the transmission map and atmospheric light, respectively. And t is defined by the exponential decay of light, expressed as / e -d(x), where the haze density coefficient 3 E [0.5. 2.0). d(x) refers to the scene depth, estimated by DepthAnything-v2 [40]. Besides, A constrained within [0.3, 0.9] for realistic simulation.
3

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Spatial-Frequency Collaborative Visual Adapter
Fast Fourler
Cosine
Transform
Simularity
T Fnstr
Visual Embedding
Text Embedding
Various User Austructions
Slide
Down Sample
Visual Encodiee
Pool
Pyts
Prexe
33
Dr. Dd
MLP
Restoration
Encoder
Prompt Monulated Module
Transformer-best
Transformer-
GR-ATT
Promph-uudded
Decoder
based Feature
domale
2OD Entanncement
QKV
Extraction
Fusion
SE-TTT
Dir
Semple
Sumple
Loss Punation
Encader and Fusion Layer
Prompt-Moculated Module
Traneformer-based Decoder
Down Somple
Sample
Encader and Fusion Layer
Prompt-Modulated Module
D
Sample
Transformer-based
Encoder and Fusion Layer
Prompi-Modulatedd Module
Figure The overall framework our controllable image fusion network.
Based on Eq. T. construct a multi-modal composite Degradation Dataset with four Levels (DDL-12), comprising 12 distinct degradations. We selecte 2, 050 high- clear images from RoadScene [6), LLVIP 7. and MSRS [29] datasets. Subsequently, the degraded imaging model is employed to synthesize degraded images with 12 types and 4 levels of degradation. Finally, the DDL-12 dataset contains approximately 48, 000 training image pairs and 4, 800 test image pairs.
4 Methodology
4.1 Problem Formulation
Given two source images Jir E Hxxx1 and T, E RH the typical fusion paradigm employs a network N3 to synthesize the fused image Iy, expressed as: T (Trr, Inx). However, in complex scenarios, source images usually suffer from degradation interference, thus the advanced fusion paradigm must take both image restoration and fusion into account. While the concatenation approach is straightforward, it does not model recovery and fusion
end-to-end optimization we propose a controllable paradigm
process, yielding suboptimal outcomes. As illustrated in Fig. 2 that couples restoration and fusion with degradation prompts, termed ControlFusion, overcome this limitation. On the one hand, the coupled approach enhances task synergy. On the other hand, leveraging degradation prompts modulate the restoration and fusion process enables the network to adapt to diverse degradation distributions while meeting user-specific customization requirements. The proposed restoration and fusion paradigm can be defined I; (Tury Tui, Ω)), where Nrf is restoration and fusion network, denotes degradation prompts, and Ω indicates the degradation set. Our ControlFusion employs two-stage optimization protocol. Stage aligns textual prompts with visual embedding, while Stage II optimizes the overall framework.
4.2 Stage I: Textual-Visie Prompts Alignment
Spatial-Frequence Collaborative Visual Adapter. To achieve unified multimodal degradation representation, we first employ text semantics for explicit degradation modeling. Using pre- trained CLIP text encoder &, the user instruction Tinstr is converted into text embedding: Ptezt (Tinsstr). However, text-dependent models limit deployment flexibility. We therefore develop a opatial-frequeee collaborative visual adapter (SFVA) to extract visual embeddings directly from images while maintaining semantic alignment with Pteat-
As shown Fig. 3, degraded images exhibit distinct spectral characteristics, indicating frequency features contain rich degradation priors. Our SFVA contains dual branches: In the frequency branch,

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Visible
Noise
Infrared
Stripe Noise
Contrast
Spatial Domains 14 Domains
Frequency
Figure 3: The visualization of various types of degradation in the spatial and frequency domains.
Fast Fourier Transform (FFT) and CNN extract frequency features:
fre = - Dm (I. v) f2x 11, Frrr= Linear (Conv (Fiife Ffte)) Fitd)))
where [ denotes channel-wise concatenation. The spatial branch employs cropping/downssmpling augmentation and CNN to extract spatial features P'spa-
The fused visual embedding Peis is obtained through concatenation and linear projection of Fre and Fepa. To ensure semantic consistency between visual/text embeddings, we apply MSE loss Lmse and cosine similarity loss Lcos:
C, = >1 llpuis - Ptezt +h2 (1
puia Preant
Priss ll
Ptext
where \ and A2 balance loss components. This design enables automatic degradation adaptation while preserving text-aligned semantics.
4.3 Stage II: Prompt-modulated Restoration and Fusion
4.3.1 Network Architectures
we devise the hierarchical transformer- encoders to extract multi-scale feature representations from degraded infrared (D,r) and visible (D.t) images separately, which is formulated as: (Fir. For] Enr (Dir), Eni (Dus), where & and E infrared and visible image encoders.
Feature Encoding and Fusion Layer. As shown Fig. 2
Furthermore, achieve comprehensive modal feature integration, design the intra-domain fusion unit, where the cross-attention (CR-ATT) mechanism is employed to facilitate interaction between features across modalities. Specifically, the linear transformation functions Foke and Foke project Für and F into their corresponding Q. K, and V. expressed as:
(Qir, Kir, Vir) = = (Fir), (Qui, Kni, Vni) = Fakk (Fwz).
(8)
Subsequently, swap the queries Q of complementary modalities to promote spatial interaction:
(uskir) Vdx
QirtKus
FF softmax
Vir, F -sftmaxxx
Vni
Vdx
where de is scaling factor. We concatenate Fjr and Fj to get fusion features: =y [Fjr. FyJ.
Prompt-modulated Module and Image Decoder. To dynamically adapt fusion feature distri- butions degradation patterns and degrees, propose Prompt-Modulated Module (PMM) for robust feature enhancement. First, sequential MLPs (œp) derive distributional optimization parameters:/7yp 8p] = (p)),, where p {pris Prext. Then, 7p and 3p applied for feature scaling and bias shifting in a residual manner:F, (1 + 7p) @ Fy + 3p, where @ denotes the Hadamard product, and F, indicates enhanced features incorporating degradation prompts. We further deploy series of Transformer-bar based decoder D, with the self-attention to progressively reconstruct the fused image I = Dy(F)) partieular, PMM and DD are tightly coupled in multi-stage process, effectively incorporating fusion features and degradation prompts to synthesize the desired image.
5

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4.3.2 Loss Functions
Following the typical fusion paradigm [20]. we introduce the intensity loss, structural similarity (SSIM) loss, maximum gradient loss, and color consistency loss constrain the training of Stage II.
The intensity loss Line maximizes the target prominence of fusion results, defined as:
Line 1
Hw y max Ihg) 10))l.
(10)
where 1 and pay are the high-quality source images.
The structural similarity loss fissi ensures the fused image maintains structural consistency with the high-quality source images, preserving essential structural information, and formulated as:
Lysim
(I, !! + (IIM ))
(11)
The maximum gradient loss Lgrad maximizes the retention of key edge information from both source images, generating fusion results with clearer textures, formulated as:
Larad
HW | - max th V )
(12)
where V denotes the Sobel operator. Morcover, the color consistency loss Lootor ensures that the fusion results maintain color consistency with the visible image. We convert the image to YCbCr space and minimize the distance between the Cb and Cr channels, expressed as:
Lootor Mw (Lr) (4.9)((
(13)
where Faser denotes the transfer function of RGB to CbCr.
Finally, the total loss L11 for Stage II is the weighted sum of the aforementioned losses: Lu aint Lint + asssm Leeim + aigrad Lgrad + acolor Lcolor,
(14)
where Oignts assmm Orgrad and Ocotor are hyper-parameters.
5 Experiments
5.1
Implementation and Experimental Configurations
Our image restoration and fusion network is built on four-stage encoder-decoder architecture, with channel dimensions increasing from 48 to 384, specifically configured as [48, 96, 192, 384]. The model is trained on the proposed DDL-12 dataset. During training. 224 x 224 patches are randomly cropped as inputs, with a batch size of 12 over 100 epochs. Optimization is performed using AdamW, starting with a learning rate of x 10-3 and decayed to 1 x 10 via cosine annealing schedule. For the loss configuration, A and A2 are set with a weight ratio of 1 : 3, and C'ints Osssis Ogradd and Ocoooor are assigned values of 8 1: 10: 12, respectively.
We introduce text prompts to enable the unified network effectively handle diverse and complex degradations. Each prompt specifies the affected modality, the degradation type, and its severity, enabling user-controllable flexibility. For example, typical prompt for single degradation is: We are performing infrared and visible image fusion, where the modality suffers from a grade- severity degradation type. For composite degradations, we extend this template to specify multiple degradation modality-degradaatio pairs, e.g., We are performing infrared and visible image fusion. Please handle a grade-seven -everitytt degradation type-A in the modality-A, and a grade-severity-B degradation type-B in the modality-B B. Details of the prompt construction paradigm are provided in the Appendix.
We compare our method with seven SOTA fusion methods, i.e., DDFM [50], DRMF (1., EMMA [51]. LRRNet [T]. SegMiF [6]. Text-IF (39,, and Text-DiFuse 42 Firstly, we evaluate the fusion performance on four widely used datasets, i.e., MSRS 29 LLVIP 77). RoadScene (35) and FMB [16]. The test set sizes for MSRS, LLVIP, RoadScene, and FMB are 361, 50, 50, and 50, respectively, Four metrics, i.e., EN, SD, VIF, and Qubf. are used quantify fusion performance. Additionally, we evaluate the restoration and fusion performance across various degradations, including oww-conrastt, random noise, and stripe noise infrared images (IR), as well as blur, rain, over-exposure, and low light in visible images (VI). Additionally, we evaluate its effectiveness in handling composite degradation scenarios. Each degradation scenario includes 100 image pairs from DDL- 12 dataset. We use CLIP-IQA (44, MUSIQ 9 TReS 3, EN, and SD to quantify both restoration and fusion performance. All experiments are conducted on NVIDIA RTX 4090 GPUs with an Intel(R) Xeon(R) Platinum 8180 CPU (2.50 GHz) using the Torch framework.

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Table I: Quantitative comparison results typical fusion datasets. The best and second-best results are highlighted in Red and Purple.
MSRS
LLVIP
RoadSeene
Methods
EN
SD
VIF
Qabí
EN
SD
VII
Qubf
EN
SD
VIF
Qabf
EN
SD
VIF
Qubf
DDFM
6.431
47,815
0.844
0.643
6.914
48.556
0.693
0.517
6,994
47.054
0.775
0.595
6.426
40.597
0.495
0.442
DRMF
6.268
45.117
0.669
0.550
6.901
50.736
0.786
0.626
6.231
44,221
0.728
0.527
6.842
41.816
0.578
0.372
EMMA
6.747
52.753
0.886
6.366
47.065
0.743
0.664
0.436
LRRNet
6.761
49,574
0.713
0.667
6.191
48.336
0.864
0.575
7.185
46,400
0.756
0.658
6.432
48. 18.151
0.501
0.368
SegMiF
7.006
57:073
0.764
7.260
45.892
0.539
0.459
6.736
48,975
0.629
0.584
6.363
0.539
0.482
Text-FF
6.619
55.881
0.753
0.656
6. 6.364
49.868
0.859
0.566
6,836
47.596
0.634
0.609
7.397
47.726
0.568
0.528
Text-DiFuse
6.990
56.698
0.850
0.603
7.546
55.715
0.883
0.659
6.826
50,230
0.683
0.662
6.888
49.558
0.793
0.653
ControlFuse usion
7,340)
60.360
0.927
0.718
7.354
56.631
0.968
0.738
7,421
51.759
0.817
0.711
7.036
50.905
0.872
0.730
Table 2: Quantitative comparison results under different degradation scenarios with pre-enhancement.
VI (Blur)
VI (Rain)
VI (E light, LL)
VI (Over-sspuary OE)
Meslools
CLIP-IQA
MUSIQ
Tibs
SB
CLIIP-QAA
MUSIQ
TReS
SB
CIIP--QA
MUSIQ
TReS
SB
CIIIP-IQ4
MUSIQ
TiRS
SP
DDFM
0.141
39.421
30.047
35.411
0.191
38.836
46:285
36.376
0.156
30.455
41.782
31.259
0.143
43.167
43,440
32.059
DRAIF
0.128
40.739
10.568
40:723
0.174
45. 161166
48.565
41.174
0.143
41.428
37.947
38.297
0.190
48.334
42.581
44.256
EAIMA
0131
41:473
41:144
0.138
44.816
0.188
19.64
64.827
40.857
0.190
46.75 76731
41.616
41:347
LRRNN
0.163
43. 919
57.368
13.990
0.185
43.291
46.285
0.164
40.456
54.856
41.899
0.100
45.414
SegMiF
0.152
43.316
44.000
0.195
40.528
44.274
0.177
44.03
<8.376
44-829
0.166
49.132
38.019
484
Text-IF
0.164
44.801
45.400
41.297
47.360
0.163
41.096
<9.174
47.257
0.172
40.298
45.599
47.330
Test-DiFus
0.172
44.955
47.699
46.176
39:243
(5.017
0.192
46.74
50.125
49:833
0.183
49.596
50.279
ControlF usion
0.154
50.240
50.387
52.319
52,466
58.901
0.183
48.420
51,072
53.787
0.391
50.301
53,903
54.218
Maze,
(Lave-contrast
(Kandomn nelee,
IR (Stripe noloe, SN)
Methods
CLIPAOA
MUSIQ
TRe8
EN
CLIP-IQA
MUSIQ
TRrS
CLIP-IQA
MUSIQ
CLIP-IQA
MUSIQ
TRRe5
EN
DDFM
0.158
41.119
44.095
6.807
0.172
44, 44,490
64.565
17. 652
0.196
14.059
$7.817
6,029
0,309
48.479
$2.571
6.390
DRMF
0.131
45.534
43,603
6.230
0.218
43. 962
45.561
40.315
0.192
44.617
44.081
S744
0.187
44.714
43.690
6.354
EXMA
0.169
39.092
46.046
6.517
0.154
48. 68.724
43.113
53.861
0.172
39.666
44,466
6.184
0,153
42.822
44.239
6.362
LKKNet
0.130
45.57]
48973
7.363
0.151
41.71%
05.266
51.005
0.154
48.274
37,04)
77295
0.137
46.511
35,010
7.700
SegMitt
01147
13.
45019
7.281
0.184
44.659
51.427
41.645
0.190
42.064
19. 79.495
0.00M
0.106
19:857
79.387
0.355
Trat-Fy
01178
50.565
49:71
6.966
0.187
49 10-299
00.766
47.0713
0.300
46.441
08.491
6.256
0.161
49.019
47-755
6.05
Prat-DiFww oar
0.178
52.788
53073
7:470
0.155
50.000
51:215
79.429
0,203
49.278
09:470
5.952
0.194
55.762
40:385
7.009
ControlF undun
0.189
54.287
$4,466
7,891
0.196
$1.086
52.846
S7.827
0.189
30.711
51.868
7.724
0,200
50,097
50.364
7.619
(OE) and 1R (LC))
ViILow light and Noise, L.2)
Y1 (REE) and IR (RN)
Y1 (L.L) and IR (SN)
Methods
CLIP-IOA
MUSIQ
TRuS
SD
CLII--QA
MUSIO
TRcS
CLII--IOA
SIUSIO
TR&S
CLIP-IQA
MIUSO
TRu5
0.168
30,055
45.293
31,791
0.151
33:440)
37.342
0.189
42.630
5.716
00:347
0.174
43.003
0.142
5(2M)
LIBNNe
SeaMir
0.114
422256
33.647
42.354
31.717
37,079
6.767
Test IF
0.174
47.9%
05.343
45:82
46.545
0.140
41.429
45.220
5,325
Test-Dit Dit
0.131
49.12)
50,991
$0.775
48,100
48.655
38.008
0.161
47.730
85.448
6,73%
Controll
0.387
$0.955
SIL107
35.417
0,167
8K971
7.055
5.2 Fusion Performance Comparison
Comparison without Pre-enhancement. Table summarizes the quantitative assessment across benchmark datasets. Notably, our method attains SOTA performance in SD, VIF, and Qaby metries, showcasing its exceptional ability to maintain structural integrity and edge details. The EN metric remains competitive, indicating that our fusion results encapsulate abundant information.
Comparison with Pre-enhancement. For fair comparison, we exclusively use the visual prompts to characterize degradation in the quantitative comparison. Meanwhile, we employ several SOTA image restoration algorithms as preprocessing steps to address specific degradations. Specifically, OneRestore 66) is used for weather-related degradations, Instruct-IR (2] for illumination-relan related degradations, and DA-CLIP [8] and WDNN [4] for sensor-related degradations. For composite degradations, we select the besf-performing method from these algorithms.
The quantitative results in Tab. show that ControlFusion achieves superior CLIP-IQA, MUSIQ, and TReS scores across most degradation scenarios, demonstrating a strong capability in degradation mitigation and complementary context aggregation. Additionally, no-reference fusion metrics (SD, EN) show that ControlFusion performs on par with SOTA image fusion methods. Qualitative compar- isons in Fig. 4 indiccate that ControlFusion not only excels in addressing single-modal degradation but also effectively tackles more challenging single- and particular, when rain and haze coexist in the visible image, ControlFusion successfully perceives these degradations and eliminates their effects. multi-modal composite degradation scenarios, leverages degradation prompts to adjust the distribution of fusion features, achieving high-quality restoration and fusion. Extensive experiments demonstrate that ControlFusion exhibits significant advantages in complex degradation scenarios.
composite degradations. In
7

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Prompt: objective merge light images, handling degradattoo images
Prompl: Addressing bler degradation visible images is kay component of the infrarred-visible light image fusion procesa
Prompt merge infrared and light images, specifically doaling with image problems.
Prompt: This infrared visible light fusion čask. Visible have light degradatton and infrared have
Prompt:
The task focuses on fusing infrared and visible light images, with the visibie spectrum encountering rain and haze.
Prompt: intrared visible images, visible image
image
SegMIF (ICCV 231)
ConbrolFusion (Durs)
(Deg lEn.)
(En.)
(ICCV 29')
23))
(ACMMM 24)
(CVPR
(CVPR 24')
(NauriPS
Figure Visualization of fusion results under different degradation scenarios.
Infrared
Visible
(c) Text-DiFuse (NeurIPS (d) ControlFusion
Figure Generalization results under real-world degradation scenarios.
5.3 Extended Experiments
Real-world Generalization. As shown in Tab. 3 we report compar- ative results on nighttime scenes from MSRS and the real weather-degraded benchmark AWMM-100K [13]. Our method achieves the best perfor- mance on CLIP-IQA, TRes, and SD, while slightly trailing Text-DiFuse MUSIQ On AWMM- 100k, our ap- proach consistently outperforms peting methods across all evaluation metrics under real-world weather degradation conditions.
Table 3: Quantitative comparison on MSRS nighttime scenes and AWMM-100K with woal-world weather degradations.
MSRS (Nighttime)
AWM31-180K (Weather Degradations)
Methods
CLIP-IOA
MIUSEO
1Rch
SD
CLIP-IQA
MUSIU
TR&S
80
DDEMI DRMF
12.04
25.1134
19.414
33.935
11.227
41.919
53.858
36,0111
02.002
27.518
16.052
46.718
(1.332
50.054
$6,671
39,735
EMMA
0.087
36.713
18.511
42.650
0.253
47.161
49.732
40.398
LRRNNt
0.098
27.042
16.909
42.892
0.192
46.751
50.707
57.190
SegMiF
01257
33.111
Text-1F
32,066
Text-DiPuse
ControlFusdon
57.513
In addition to quantitative comparisons, Fig. presents visual examples demonstrating our method's strong generalization ability removing composite degradations in real scenes. The top image shows typical data collected by our multimodal sensors under challenging low-light and noisy conditions,

---

Prompt:.. ... merge infrared and visibbe images, handling grade Nleeel low-lightover exposure problem
liight
wow.o
LOveni
poukes
phootppren ielue
Input IRIVI
Text- DiFuse
ControlFusiio with various levels for prompting
Brightness distribution
Figure 6: Fusion results with various level prompts.
Table 4: Computational efficiency of image restoration and fusion algorithms on 640 x 480 resolution.
Image Restoration
Image Fusion
Methods
Parm.(M) Flops(C)
Time(s)
Methods
Parm.(M)
Flops(
Time(s)
Methods
Parm.(M)
Flops(G)
Time(s)
Instruei1R
15.94
151.54
0.038
DDFM
552.70
5220.50
34.511
SegMiF
45.64
707.39
0.371
DA-CLIP
295.19
1214.55
13.578
DRMF
5.05
121.10
0.842
Text11F
152.44
1518.90
0.157
WDNN
0.01
1.11
0.044
EMMA"
1.52
41.54
0.048
Text DiFuse
157.35
4872.00
12,903
OncRestore
18.12
114.56
0.024
LRRNe1
0.05
14.17
0.096
ControlFusion
182.54
1622.00
0.183
while the bottom image illustrates the robustness of our method in handling extremely low-light scenarios. Additional qualitative results provided in the Appendix.
Flexible Degree Control. We employ numerical values to quantify the severity of degradation in design, where higher numbers indicate more severe degradation, enabling precise continuous control. should noted that while only four anchor points (1, 4, 7, and 10) were used during training, the model demonstrates remarkable generalization capability to intermediate degrees during testing, owing to the inherent encoding characteristics of CL.PP As shown in Fig. [6] with degradation level prompts, ControlFusion adapt varying degrees of degradation and deliver satisfactory fusion results. Moreover, fusion results generated using adjacent degradation-les prompts exhibit subtle yet perceptible differencees, allowing to obtain customized outcomes through their specific prompts. More visualization results are presented in the Appendix.
Computational Efficiency. Table details the computational costs for various methods. While standalone fusion models such as EMMA, LRRNet, and SegMiF, which are marked with appear more lightweight, they necessitate a separate, computationally intensive restoration stage to form a practical pipeline. When the overhead of this two-stage process is factored in, the efficiency advantages of our unified model becomes evident. Moreover, our model's efficiency remains highly competitive with other joint restoration- fusion approaches like DRMF, Text-DiFuse, and Text-IF.
Object Detection. We evaluate object detection performance on LLVIP to assess the fusion quality, using the re-
Table 5: Quantitative comparison of object detection.
Methods
Prec. Recall AP@0.50
AP@0.75
mAP@0.5:0.95
DDFM
0.947
0.848
0.911
0.655
0.592
trained YOLOv8 (26). Quantitative results are presented in Tab. 5 Our results enable the detector to identify all pedestrians with higher confidence. achieving the highest mAP@0.5-0.95. Visualizations are in the Appendix.
DRMF
0.958
0.851
0.937
0.672
0.607
EMMA
0.942
0.872
0.927
0.647
0.598
LRRNet
0.939
0.878
0.933
0.672
0.608
SegMiF
0.965
0.896
0.931
Text-IF
0.959
0.892
0.939
0.655
0.601
Text-DiFuse
0.961
0.885
0.941
0.656
0.606
ControlFusion
0.971
0.889
0.949
0.685
0.609

---

w/o Sudee desortion)
(i) wie Spret (Mesing teatures]
(101) Wib frangr gens)
IIy wel qualty
Ouro (High quality
Source images
(V Wel Frequenty qualttl
Ours
wel Freegency
In Freguency
Figure 7: Visual results of ablation studies under degradation scenarios.
Table 6: Quantitative results of the ablation studies.
VI(LL Noise)
VI(OE) and IR(LC)
VI (RH) and IR(Noise)
Configs CLIP-IQA MUSIQ
TReS
EN
CLIP-IQA MUSIQ
TReS
SD
CLIP-IQA MUSIQ
TReS
SD
0.132
42.424
42.839
4.788
0.166
41,983
42.841
39.917
0.147
43.619
47,932 48.935
11
0.152
45.582
45.358
5.855
0.151
43.646
42,002
39.208
0.167
47.862
45.007
44.347
III
0.154
46.571
44.495
5.013
0.155
44.286
44,561
42.068
0.156
43.007
43.816
41.544
41.310
5.414
41.743
39.125
38.748
0.118
48.882
46.245
V 0.173
45.281
46.291
6.279
0.181
45.386
47.519
46. 46.860
0.149
46.714
48.094 46.950
Ours
0.225
49.333
49,513
7.111
0.187
50.479
50.298
50.955
0.179
50.107
51.091 55.417
Ablation Studies. To verify the effectiveness of components, conduct detailed ablation studies, involves: (I) w/o Lentor, (II) w/o Lgrad- (III) w/o Line, (IV) w/o PMM, and (V) w/o frequency branch. As shown in Fig- removing any loss module significantly impacts the fusion quality. Specifically, without Ceotor. color distortion occurs, while removing Lyrad leads to the loss of essential texture information. Excluding Line fails to highlight thermal targets, and removing PMM diminishes the ability to address composite degradation. Additionally, removing the frequency branch from SFVA causes visual prompt confusion. The t-SNE results further validate the importance of frequency priors. The quantitative results in Tab, 6 also confirm that each design element is crucial for enhancing fusion performance.
Discussions and Limitations. To mitigate the gap between simulated and real-world data, our method constructs training samples using the degradation imaging model. An alternative strategy is test-time adaptation, in which part of the model is fine-tuned on the test set to better accommodate new data distributions. It should be noted that the degradation imaging model is specifically designed for infrared and visible image fusion and is difficult to generalize to other fusion tasks, such as medical image fusion and multi-focus image fusion.
Conclusion
This work proposes a controllable framework for image restoration and fusion leveraging language- visual prompts. Initially, develop physics-driven degraded imaging model to bridge the domain gap between synthetic data and real-world images, providing a solid foundation for addressing composite degradations. Moreover, we devise prompt-modulated network that adaptively adjusts the fusion feature distribution, enabling robust feature enhancement based on degradation prompts. Prompts either come from text instructions to support user-defined control be extracted from source images using a apatial-frequencyy visual adapter embedded with frequency priors, facilitating automated deployment. Extensive experiments demonstrate that our method excels in handling real-world and composite degradations, showing strong robustness across various degradation levels.
7 Acknowledgement
This work was supported by NSFC (62276192).
10

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A Appendix
A.1 Prompt Design Details
We provide additional details of the prompt construction paradigm to complement the main text.
Prompt components. Each prompt specifies three key components: (i) the affected modality (infrared or visible), (ii) the degradation type (e.g., rain, haze, low-light, overexposure, random noise, stripe noise, low-contrast, etc.), and (iii) the severity level (an integer from to 10).
Severity levels. To characterize degradation severity, define scale from 10, with representa- tive anchor points as follows:
Level I: barely perceptible degradation,
Level 4: degradation begins to interfere with human scene understanding,
Level degradation severely hinders human perception of the scene,
Level 10: most useful information is completely obscured.
Intermediate levels can be interpolated between these anchor points.
Prompt templates. A typical template for a single degradation is structured as: We are performing infrared and visible image fusion, where the modality suffers from grade-severity degradation rype.
For composite degradations, two extensions designed: 1) We performing infrared and visible image fusion. Please handle grade-severity-A degradation type-A in the modality-A, and a grade- severity-B degradation type-B B in the modality-B; 2) We performing infrared and visible image fusion. Please address level-severity degradation type-A and degradation type-B in the modality.
These templates flexibly represent both intra- and inter-modal combinations. For instance, instantiation could be: We are performing infrared and visible image fusion. Please handle a grade-6 low-light in the visible modality, and grade-8 8 stripe noise in the infrared modality.
Linguistic diversity. To enhance robustness, we curated over 100 unique textual formulations for each task and its associated parameters. This prompt rephrasing strategy augments linguistic variability and improves the model's generalization diverse prompt styles during inference.
Additionally, when users are uncertain about severity, the proposed SFVA module can automatically perceive both degradation type and severity from the input. This design balances user-controllable flexibility with automated practicality.
A.2 Visual Embedding versus Textual Embedding
find that both visual and textual prompts assist Nz in restoring the scene illumination. Besides, the residual plots between the two are not obvious, which indicates that our SFVA successfully captures the type and extent of degradation. This capability is crucial for automatic deployment, as it allows the model to adaptively adjust its restoration strategy with fewer manual lintervention or hyperparameters tuning for different degradation scenarios.
From Fig.
Textual prompt: This is infrared and visible image fusion task with the visible image suffers from grad #4 low-light issue
(a) Infrared
(b) Visible (Low light)
(c) Visual prompt
(d) Textual prompt
(e) Residual map
Figure 8: Comparison of visual and textual embedding.
A.3
Flexible Degree Control
In order to verify the effectiveness of the proposed flexible degree control, we perform different degrees prompts on a pair of low-light images. The results are shown Fig. (b). We splice all the results to obtain images with continuously changing brightness in different regions, and count the
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Wath (lan
Inputs
Stitching result
(c) Average brightness variation
level=2
leve
level=4
level=5
leveli6
level=7
levele8
levelig
level#10
(d) Visualization results different degradation levels
Figure 9: Fusion results with various level prompts.
brightness of the same region of these images. The results are shown in Fig. (c). This shows that our degree control achieves fine-graing regulation.
A.4 Real-world Generalization
We conduct comprehensive experiments on the FMB dataset under various composite degradation scenarios. Notably, these test data are collected from weal-world environments and represent degrada- tion patterns not encountered in the training set. During evaluation, we rely exclusively on visual prompts automatically extracted from the input images, demonstrating our method's 's ability to handle unseen degradation conditions without manual intervention, as shown in Fig. 10
A.5 Effeet of Severity Level Granularity
To ensure our model learns to discern fine-grained degradation severity, we train it on dense set of four anchor degradation levels (1&477& 10) from the DDI.-12 dataset. This design is supported by comparative analysis against models trained on sparser subsets of only two levels, specifically the moderate pair (4&7) or the extreme pair (1&10). As shown Tab. 7 these sparser training regimes lead to notable drop in generalization performance real-world scenarios, particularly when only the extreme levels (1& 10) are used. These results indicate the importance of dense sampling of training levels for robust generalization.
Table 7: Comparison of models trained with varying degradation levels on real-world datasets.
LLVIP
FMB
Level numbers
CLIP-IQA
MUSIQ
TRcS
SD
CLIP-IQA
MUSIQ
TRcS
SD
2 (1&10)
0.320
53.498
61.889
54.665
0.192
52.839
63.115
50.122
2 (4&7)
0.332
55.364
64,382
55.742
0.207
52.771
63,328
50.429
(184477810
0.347
55.890
65.014
56.657
0.208
53.034
63.704
51.707
A.6 Object Detection
As illustrated in Fig. 11 our fusion method achieves superior object detection performance with consistently high confidence scores. Notably, the detector successfully identifies all pedestrians in challenging scenarios, including heavily occluded cases where other methods fail. These results
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Infrared
(b) Visible
Text-DiFuse
(d) ControlFusion
Figure 10: Real-world composite degradation scenario lesting.
(a) Infrared
(b) DDFM
(c) DRMF
(d) EMMA
(e) LRRNet
(f) Visible
(g) SegMiF
(h) Text-IF
Text-DiFuse
(i) ControlFusion
Figure 11: Visualization results of target detection.
quantitatively validate the effectiveness our approach in preserving and enhancing critical visual information through the fusion process, particularly for small and obscured targets. The improved detection performance further confirms that our method maintains both thermal signatures and texture details essential for downstream vision tasks.
A.7 Model Complexity Analysis
As demonstrated in Fig. and Tab. 6 both CLIP and SFVA (i.e., visual-text prompt alignment) yield substantial improvements in restoration and fusion performance. We analyze the computational cost of these components in Tab. 8 Although CLIP accounts for significant number of parameters (102M, 55.9% of the total), they are frozen during training and thus introduce no training overhead. In terms of inference cost, CLIP and SFVA are responsible for modest 0.8 and 7.5% of the total
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Table 8: Overhead of individual components. Percentages indicate the relative share in the full model.
Components
Parm. (M)
FLOPPs (G)
Time (S)
CL1-
102,00 (55.88) 88%)
13.088 (0.81%)
0.0120 (6.55%)
SFVA
15:27 (8.37%)
122.32 (7.54%)
0.0067 (3.66%)
Full Model
182.54
1622.00
0.1833
FLOPs, respectively, and their combined runtime constitutes small fraction of the total. Overall, both components offer compelling trade-off, delivering notable performance gains for moderate computational expense.
24


Reviews:
[{"review_id": "n87u1IWPvl", "reviewer": "Reviewer_JT4e", "summary": "In this paper, the authors aim to fuse infrared and visible images under complex, multi-stage degradations. Specifically, these degradations stem from illumination, weather, and sensor effects. Based on this modeling process, they created a synthetic training dataset. The training also incorporates both degradation prompts and automatic degradation detection, so that the text prompt and the degraded image each provide cues about the degradation.", "questions": "- Have you evaluated how the number of degradation intensity levels used during training affects your model’s generalization on real‐world datasets (e.g., FMB, LLVIP)? For instance, if you change from 4 levels to 2, 3, or more, how does the performance vary?\n\n- How are those pre-enhancements applied? Line 227 just mentions the application of Instruct-IR and OneRestore. In Table 2, do you use only one of them, or different fusion methods has different pre-enhancement method? Meanwhile, have the authors tried applying degradation-specific SOTA methods sequentially as pre-enhancement?\n\n- How do the authors ensure that the training data domain covers the degradations present in real-world datasets? For example, the synthetic dataset simulates rain as streaks, but in reality, raindrops are much more common and challenging. Similarly, the haze model used here is homogeneous, yet varying the depth scale and hyperparameters can produce a wide range of haze effects. How can the proposed dataset and method handle such complex real-world data given the limited diversity in the training set?\n\n- It seems that the real-world testing does not include weather degradations. Can the authors justify this omission?\n\n- How can the infrared branch data provide insightful observations that can be implicitly or explicitly leveraged in this fusion task? It seems the authors simply enlarged the training dataset and helped the model learn everything without considering sensor-specific priors that could be exploited.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "- Strength:\nThe consideration of multi-stage degradations appears relatively new for image fusion tasks, and the proposed method explicitly addresses this, improving robustness in real-world scenarios.\n\nAs reported, the proposed method is effective on both synthetic and real-world datasets.\n\n- Weakness\nThe source of its performance on real-world datasets is questionable, given the training data's limited diversity. Additionally, the real-world tests do not include weather-related degradations.\n\nDesign-wise, the method primarily leverages data priors by constructing a multi-degradation paired dataset, yet it completely overlooks potential priors inherent in the infrared data format, making the overall solution less insightful.", "was_revised": true, "final_justification": "Even though the diversity of real-world testing is limited, the method demonstrates solid performance on the target task. Most of my concerns have been addressed by the authors through additional experiments.\n\nTherefore, I will maintain my original score of weak accept, as I believe it reflects the quality and contribution of this paper.", "rebuttal": "# Q1: Effect of Severity Level Number \n>Thanks for the valuable comment. As shown in **Table r1**, we compared models trained with all 4 anchor levels (1, 4, 7, 10) against those trained with only 2 levels (1 & 10 or 4 & 7). Reducing the number of degradation levels weakens the model’s generalization ability on real-world datasets, particularly when only extreme levels (1 & 10) are used. This limits the model’s capacity to accurately perceive fine-grained degradation severity. Due to limitations in time and dataset (DDL-12 has 4 levels: 1, 4, 7, and 10), we are currently unable to explore more settings as suggested (e.g., 3 or 5-6 levels), but will investigate them in the camera-ready.\n\n**Table r1.** Comparison of models trained with varying degradation levels in real-world datasets.\n||FMB|||LLVIP||||\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|Level numbers|2(1,10)|2(4,7)|4(1,4,7,10)|2(1,10)|2(4,7)|4(1,4,7,10)|\n|CLIP-IQA|0.192|0.207|0.208|0.32|0.332|0.347|\n|MUSIQ|52.84|52.77|53.03|53.5|55.36|55.89|\n|TReS|63.12|63.33|63.7|61.89|64.38|65.01|\n|SD|50.12|50.43|51.71|54.67|55.74|56.66|\n# Q2: Pre-enhancement Method\n>Thanks for the thoughtful question. In **Table 2**, **for each degradation type**, we apply the **same pre-enhancement method** for all fusion models without built-in enhancement modules. Specifically, **OneRestore** is used for weather-related degradations, while **Instruct-IR** is applied to illumination and sensor-related degradations. For IR stripe noise, we additionally employ **WDNN** [r1] as preprocessing, given the scarcity of effective stripe noise removal methods. We will clarify this in camera-ready.\n\n>As reported in **Table r2**, we further provide comparison results using SOTA degradation-specific pre-enhancement methods under various degradation scenarios. Specifically, DRMF, Text-IF, and Text-DiFuse adopt their own built-in enhancement modules in low-light conditions. For rain, haze, and rain&haze scenarios, we employ additional pre-enhancement algorithms, as these methods lack native support for such degradations. In particular, we use **HVI-CIDNet (CVPR'25)** [r2] for low-light enhancement, **NeRD-Rain (CVPR'24)** [r3] for de-raining, and **IPC-Dehaze (CVPR'25)** [r4] for dehazing. While these SOTA methods can improve fusion quality to some extent under specific degradation scenarios, our method consistently achieves superior overall performance. This is mainly due to our ability to **jointly exploit complementary information across modalities**, enabling more accurate scene representations and better approximation of high-quality imagery.\n>Furthermore, as shown in **Tables r2(d)** and **r2(e)**, we explore the impact of sequential processing orders under compound degradations. Results indicate that the processing order does affect fusion performance to some extent, especially in the rain&haze scenario, where addressing rain before haze yields moderate performance gains.\n\n**Table r2.** Quantitative results in various degraded scenarios with degradation-specific SOTA pre-enhancement methods. **Bold**: best; *Italic*: second-best.\n|Scenarios|Metrics|DDFM|DRMF|EMMA|LRRNet|SegMiF|Text-IF|Text-DiFuse|Ours|\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|**(a) VI(LL)**|CLIP-IQA|0.16|0.14|0.17|0.16|*0.18*|0.16|**0.19**|*0.18*|\n|**HVI-CIDNet**|MUSIQ|40.24|41.43|40.81|42.14|44.81|41.10|*46.73*|**48.42**|\n|**(CVPR'25)**|TReS|42.42|37.95|46.11|35.19|47.89|49.17|*50.13*|**51.07**|\n||SD|33.68|38.29|42.94|44.57|47.23|47.26|*49.88*|**53.79**|\n|**(b) VI(Rain)**|CLIP-IQA|**0.21**|0.18|0.15|0.19|0.19|0.17|0.18|*0.20*|\n|**NeRD-Rain**|MUSIQ|40.42|*49.07*|47.21|45.90|43.19|42.87|41.95|**52.32**|\n|**(CVPR'24)**|TReS|47.64|*49.18*|46.10|44.14|*50.64*|48.13|50.44|**52.47**|\n||SD|38.20|*42.87*|45.18|47.37|45.10|*50.12*|48.06|**50.90**|\n|**(c) VI(Haze)**|CLIP-IQA|0.21|*0.24*|0.23|0.16|0.19|0.18|0.22|**0.26**|\n|**IPC-Dehaze**|MUSIQ|44.72|**47.75**|42.39|45.26|*46.50*|44.09|40.15|45.99|\n|**(CVPR'25)**|TReS|48.74|*48.88*|39.51|46.07|47.57|48.21|42.67|**49.71**|\n||EN|7.01|*7.43*|*7.70*|7.18|6.98|7.61|7.29|**7.81**|\n|**(d) VI(Rain&Haze)**|CLIP-IQA|0.16|*0.18*|0.17|0.17|0.15|*0.18*|*0.18*|**0.19**|\n|**IPC-Dehaze→NeRD-Rain**|MUSIQ|44.84|46.79|42.18|50.01|48.49|51.79|*53.39*|**54.29**|\n||TReS|45.74|44.71|47.89|49.03|47.44|51.24|*53.84*|**54.47**|\n||EN|7.02|*6.71*|6.84|7.58|*7.64*|7.13|*7.59*|**7.89**|\n|**(e) VI(Rain&Haze)**|CLIP-IQA|0.16|*0.18*|0.17|0.17|0.16|*0.18*|*0.18*|**0.19**|\n|**NeRD-Rain→IPC-Dehaze**|MUSIQ|45.01|*46.95*|42.48|49.94|48.78|52.02|*53.02*|**54.29**|\n||TReS|45.99|*45.13*|48.04|49.35|47.71|51.30|*53.73*|**54.47**|\n||EN|6.99|*6.80*|6.92|7.52|*7.73*|7.20|7.62|**7.89**|\n\n[r1] Guan J, et al. Wavelet deep neural network for stripe noise removal[J]. IEEE Access, 2019, 7: 44544-44554.\n\n[r2] Yan Q, et al. Hvi: A new color space for low-light image enhancement[C]. CVPR. 2025:5678-5687.\n\n[r3] Chen X, et al. Bidirectional multi-scale implicit neural representations for image deraining[C]. CVPR. 2024: 25627-25636.\n\n[r4] Fu J, et al. Iterative Predictor-Critic Code Decoding for Real-World Image Dehazing[C]. CVPR. 2025: 12700-12709.\n# Q3: Real-world Datasets\n>Thanks for the insightful comment. In dataset construction, we carefully modeled degradations across sensor-related artifacts, illumination changes, and weather conditions to approximate real-world scenarios. Still, **some degradations (e.g., extreme weather), remain challenging to simulate accurately with our current imaging model, which is an open issue shared across existing methods.**\n\n>In Eq.(5), $D_{vi}^{w}=P_{w}(I_{vi})=I_{vi}\\cdot t+A(1-t)+R$, we provide a **general formulation** for weather-related degradations, where $R$ denotes the rain component. It typically manifests as rain streaks or raindrops. While we implement rain streaks, the model is readily extensible to raindrops or other patterns, ensuring flexibility to simulate more complex weather effects.\n\n>We adopt a standard homogeneous haze model, a common practice in many methods [r5, r6], due to its physical soundness, controllability, and reproducibility. To enhance diversity, we simulate a range of haze effects by using various depth maps, varying transmission functions, and multiple parameter settings (e.g., $\\beta\\in[0.5,2.0],A\\in[0.3,0.9]$). Despite its simplicity, this model exhibits promising generalization in real-world hazy scenes, as shown in **Figure 10 (Appendix)** and **Table r4**(AWMM-100k [r7]). We acknowledge its limitations, and will explore more complex variants, such as non-uniform haze (e.g., patch-wise variable $\\beta$) or learning haze distributions from real foggy images.\n\n>Although DDL-12 covers only specific degradation types, it spans diverse **degradation patterns** from 3 complementary dimensions, i.e., sensor, illumination, and weather. This structured modeling provides our model with diverse and robust priors for generating high-quality fusion results. Besides, rather than tailoring to specific degradations in the training data, our model is designed to generalize beyond specific synthetic priors. For instance, although our dataset includes only rain and haze, the learned weather priors enable the model to handle unseen weather-related degradations such as snow. As shown in **Table r3**, our method achieves promising performance on snow scenarios from the AWMM-100k benchmark, demonstrating its robustness and generalization capability.\n\n**Table r3.** Quantitative results on snow scenarios from AWMM-100k.\n|Methods|CLIP-IQA|MUSIQ|TReS|SD|\n|:-:|:-:|:-:|:-:|:-:|\n|DDFM|0.27|43.45|48.16|23.61|\n|DRMF|*0.33*|*56.47*|*53.73*|32.41|\n|EMMA|0.32|48.99|38.39|30.73|\n|LRRNet|0.31|47.03|51.99|23.59|\n|SegMiF|0.29|50.31|44.98|28.98|\n|Text-IF|0.31|51.03|49.44|31.46|\n|Text-DiFuse|0.27|52.25|43.38|*36.80*|\n|Ours|**0.36**|**57.30**|**54.88**|**38.52**|\n\n[r5] Fattal R. Single image dehazing[J]. ACM TOG, 2008, 27(3):1-9.\n\n[r6] He K, et al. Single image haze removal using dark channel prior[J]. IEEE TPAMI, 2010, 33(12):2341-2353.\n\n[r7] Li X, et al. All-weather multi-modality image fusion: Unified framework and 100k benchmark. arXiv preprint arXiv:2402.02090 (2024).\n\n# Q4: Weather Degradations\n>Thank you for the question. As shown in **Figure 10 (Appendix)**, we provide results under haze conditions, a common weather-related degradation. Existing multi-modal datasets do not include rain scenarios, and due to exposure time limitations, our multimodal sensors cannot effectively capture real rain streaks. Therefore, we are currently unable to present results under real rain degradation. To further demonstrate our method's effectiveness under real-world weather degradations, we conducted experiments on **AWMM-100k** [r7], a multi-modal dataset with real-world adverse weather. As shown in **Table r4**, our method achieves clear advantages under real-world adverse weather conditions.\n\n**Table r4.** Quantitative results on real-world weather degradations from AWMM-100k.\n|Methods|CLIP-IQA|MUSIQ|TReS|SD|\n|:-:|:-:|:-:|:-:|:-:|\n|DDFM|0.23|41.92|53.86|36.01|\n|DRMF|0.23|50.05|56.67|39.73|\n|EMMA|0.25|47.16|49.72|40.40|\n|LRRNet|0.19|46.75|50.71|37.19|\n|SegMiF|*0.26*|*50.71*|54.31|33.31|\n|Text-IF|0.21|45.63|51.75|32.09|\n|Text-DiFuse|0.23|46.90|*57.71*|*41.53*|\n|Ours|**0.28**|**57.51**|**60.30**|**44.24**|\n\n# Q5: Sensor-Specific Priors\n>Thank you for your insightful comment. Our method does not solely rely on enlarging the training dataset but strategically leverages sensor-specific priors. First, we incorporate infrared-specific characteristics, such as stripe noise, when constructing degraded infrared data, enabling the model to effectively handle modality-specific degradations. Second, our fusion model implicitly exploits high-quality infrared properties, such as high contrast, to guide visible image restoration tasks like low-light enhancement and dehazing, where contrast enhancement is a key objective. These designs ensure that sensor-specific information is effectively utilized, beyond merely scaling the dataset."}, {"review_id": "SGPeE7zfEa", "reviewer": "Reviewer_wXp7", "summary": "This paper proposes a controllable fusion network guided by language-vision prompts, called ControlFusion. The main contributions include two aspects. First, they construct a degraded imaging model based on physical mechanisms, to simulate data with composite degradation for learning. Second, a prompt-modulated restoration and fusion network is designed to dynamically enhances features according to degradation prompts. As a result, this work can adapt to varying degradation levels, showing promising robust fusion performance. They conduct extensive experiments to prove the advantages of ControlFusion.", "questions": "1) In Figure 2, does \"composite degradation\" refer to the simulated data? The logical relationship among (I), (II), and (III) is somewhat unclear.\n2) In Figure 3, the authors provide the frequency spectrum of images containing various types of degradation; it would be better to additionally present the frequency spectrum of the images without degradation for comparison.\n3) Does training the model without the proposed DDL-12 dataset degrade its performance in real-world degradation scenarios?", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\n1) Datasets involving composite degradation are highly valuable for advancing research in multimodal image fusion. This work customizes simulated imaging based on physical models, which facilitates the batch generation of the required data.\n2) This work uses text as a degradation prompt, which specifies not only the type but also the level of degradation. This provides a highly flexible interface for controlling the fusion process. It is also very meaningful for the deployment of multimodal image fusion models in real-world degraded environments.\n3) The proposed spatial-frequency visual adapter is interesting, as it enables the perception of degradation distribution within source images based on their frequency characteristics, which is crucial for subsequent information restoration and fusion.\n4) The conducted experiments are relatively comprehensive and effectively demonstrate the advantages and distinctive features of the proposed method.\n\nWeaknesses:\n1) One notable feature of the proposed method is its ability to specify the level of degradation. However, it is unclear how users are expected to have prior knowledge of the degradation severity. This could limit the practical utility of the interface. It would be helpful if the authors could provide guidance for determining appropriate degradation levels. In addition, the authors should provide a standardized paradigm for constructing textual prompts to facilitate the reproducibility of the proposed method.\n2) The imaging model presented in Section 3 is based on existing physical models, but its core innovation should be further emphasized. It is recommended that this part be integrated into the methodology section to strengthen its connection to the overall contribution of the proposed method.\n3) This work constructs degraded data based on existing datasets such as MSRS, RoadScene, and LLVIP, assuming that the original data are clean. However, if the original data are not clean, how is this issue addressed? Would the trained models be affected by the inherent noise or degradation in the base datasets?\n4) In the “Comparison without Pre-enhancement” Section, the qualitative comparison on the four mentioned datasets is lacking.\n5) In the “Comparison with Pre-enhancement” Section, it would be better to include full-reference fusion metrics, since relying solely on some perceptual metrics such as SD and EN cannot fully reflect fusion performance under degraded scenarios.\n6) To more comprehensively demonstrate the real-world reliability of the proposed method, please include real composite degradation data, such as the nighttime scenes from the MSRS dataset.\n7) Average running time is recommended to provide comparative evaluations of computational efficiency.", "was_revised": true, "final_justification": "The authors have addressed most of my concerns. And I think this is a good paper. So I suggest to accept this paper.", "rebuttal": "# Q1: Prompt Construction Paradigm \n> Thank you for the insightful comments. Some key degradation severity levels are defined as follows:\n- **Level 1**: Barely perceptible degradation.\n- **Level 4**: Degradation begins to interfere with human scene understanding.\n- **Level 7**: Degradation severely hinders human perception of the scene.\n- **Level 10**: Most useful information is completely obscured.\n\n> Other levels allow users to interpolate between these anchor levels to describe intermediate degradations. \n \n> Moreover, when users are unsure about the degradation severity, our method supports a fully automatic mode: the **SFVA** can be used directly to perceive the degradation type and severity from inputs. This design balances **user-controllable flexibility** with **automated practicality**, ensuring both fine-grained control and real-world usability.\n\n> Our prompts are designed to guide the fusion network by indicating the **modality** affected, the **degradation type**, and its **severity**.  \n\n> A typical prompt template for a **single degradation** is: \"We are performing infrared and visible image fusion, where the `modality` image suffers from grade-`severity` `degradation type` problem.\"\n\n> For **composite degradations**, we adopt extended templates such as: \"We are performing infrared and visible image fusion. Please handle grade-`severity A` `degradation type A` in the `modality A` image, and grade-`severity B` `degradation type B` in the `modality B` image.\"\n\n> Further details on **Prompt Construction Paradigm** are provided in our response to **Reviewer (iEbx) Q3**.\n\n# Q2: Imaging Model Contribution\n>We appreciate the reviewer's suggestion. Unlike existing imaging models that focus on isolated degradations, our proposed unified imaging model integrates three physical degradation paradigms and accounts for various modality characteristics. This enables accurate representation of complex, composite degradations and provides enriched data for model training, thereby helping to bridge the gap between synthetic data and real-world degradations. **We will further emphasize this key contribution and integrate the imaging model into the methodology section to better highlight its relevance to the overall framework.**\n\n# Q3: Training Data Construction\n>Thank you for the thoughtful comment. To address potential degradations in the base datasets, we use the high-quality counterparts provided by Text-IF [r1] as clean references. Thus, the training process is not affected by inherent noise or degradation in base datasets.\n\n[r1] Yi, Xunpeng, et al. Text-if: Leveraging semantic text guidance for degradation-aware and interactive image fusion. CVPR. 2024: 27026-27035.\n\n# Q4: Qualitative Comparison without Pre-enhancement\n>We regret the omission of qualitative comparisons. We have conducted qualitative assessments without pre-enhancement on the four mentioned datasets, where our method more effectively preserves salient objects from IR and retains rich texture details from VI, with these benefits also evident in qualitative comparisons with enhancement. To provide a more intuitive evaluation of fusion performance, we will include the qualitative comparisons in the Appendix.\n\n# Q5: Full-reference Metrics\n>Thank you for the valuable suggestion. We report full-reference metrics **MI**, **Qabf**, and **VIF** for various algorithms across three representative degraded scenarios in **Table r1**. Our method consistently outperforms others in most cases, further validating its effectiveness. We will include full-reference metrics for all degraded scenarios in the camera-ready version.\n\n**Table r1.** Comparison of full-reference metrics in various degraded scenarios. **Bold**: best; *Italic*: second-best.\n|Methods|VI(LL&Noise)|||VI(OE)&IR(LC)|||VI(RH)&IR(Noise)|||\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:\n||MI|Qabf|VIF|MI|Qabf|VIF|MI|Qabf|VIF|\n|DDFM|2.27|0.40|0.57|2.28|0.39|0.53|1.86|0.37|0.58|\n|DRMF|1.89|0.38|0.59|1.94|0.42|0.58|2.18|0.39|0.67|\n|EMMA|2.38|0.41|*0.70*|2.19|0.48|0.62|2.37|0.42|0.48|\n|LRRNet|1.95|0.51|0.62|2.41|0.47|0.61|2.20|0.50|0.56|\n|SegMiF|2.52|0.47|0.68|2.38|*0.52*|**0.73**|2.48|0.47|0.68|\n|Text-IF|*2.72*|*0.56*|0.61|*2.61*|0.50|0.68|2.52|0.53|0.66|\n|Text-DiFuse|2.50|0.50|0.66|2.48|*0.52*|0.69|**2.70**|*0.54*|*0.70*|\n|Ours|**2.75**|**0.59**|**0.73**|**2.70**|**0.58**|**0.73**|*2.66*|**0.58**|**0.71**|\n\n# Q6: Real Composite Degradation\n>Thanks for the insightful suggestion. As shown in **Table r2**, we provide comparative results on nighttime scenes from the MSRS dataset. Our method achieves the best performance on **CLIP-IQA**, **TRes**, and **SD** metrics, while slightly trailing Text-DiFuse on the MUSIQ metric. \n\n>Furthermore, we conducted quantitative comparisons on the real weather-degraded benchmark **AWMM-100k** [r2], with the results shown in **Table r3**. Our method outperforms others across all metrics under real-world weather degradation conditions.\n\n>The above results collectively demonstrate the real-world reliability of our method in handling real composite degradations.\n\n**Table r2.** Quantitative results on nighttime scenes from MSRS.\n|Methods|CLIP-IQA|MUSIQ|TReS|SD|\n|:-:|:-:|:-:|:-:|:-:|\n|DDFM|0.10|25.03|19.40|33.94|\n|DRMF|0.10|27.52|16.05|46.72|\n|EMMA|0.09|26.71|18.51|42.66|\n|LRRNet|0.10|27.04|16.91|42.69|\n|SegMiF|0.09|27.40|*21.08*|44.31|\n|Text-IF|0.10|28.16|20.64|*49.75*|\n|Text-DiFuse|*0.12*|**28.62**|18.97|43.73|\n|Ours|**0.14**|*28.46*|**21.61**|**52.93**|\n\n**Table r3.** Quantitative results on real-world weather degradations from AWMM-100k.\n|Methods|CLIP-IQA|MUSIQ|TReS|SD|\n|:-:|:-:|:-:|:-:|:-:|\n|DDFM|0.23|41.92|53.86|36.01|\n|DRMF|0.23|50.05|56.67|39.73|\n|EMMA|0.25|47.16|49.72|40.40|\n|LRRNet|0.19|46.75|50.71|37.19|\n|SegMiF|*0.26*|*50.71*|54.31|33.31|\n|Text-IF|0.21|45.63|51.75|32.09|\n|Text-DiFuse|0.23|46.90|*57.71*|*41.53*|\n|Ours|**0.28**|**57.51**|**60.30**|**44.24**|\n\n[r2] Li, Xilai, et al. All-weather multi-modality image fusion: Unified framework and 100k benchmark. arXiv preprint arXiv:2402.02090, 2024.\n\n# Q7: Computational Efficiency\n>Thanks for the valuable suggestion. **Table r4** reports the computational efficiency of various methods with enhancement, including parameters, FLOPs, and runtime. Although **our method is not the most efficient, it maintains acceptable inference efficiency, especially compared with joint restoration-fusion methods such as DRMF, Text-DiFuse, and Text-IF.**\n\n**Table r4.** Computational efficiency comparison with pre-enhancement. For fusion methods without restoration, we add the average cost of Instruct-IR and OneRestore.\n| |Methods|Parm.(M)|Flops(G)|Time(s)|\n|:-:|:-:|:-:|:-:|:-:|\n|**Restoration**|Instruct-IR|16.0|6.7|0.017|\n||OneRestore|6.0|1.3|0.004|\n|**Fusion**|DDFM|563.7|5224|34.51|\n||DRMF|170.9|4575|0.842|\n||EMMA|*12.5*|*45.5*|**0.048**|\n||LRRNet|**11.0**|**18.2**|*0.096*|\n||SegMiF|56.0|358|0.158|\n||Text-IF|89.0|302|0.157|\n||Text-DiFuse|157.4|4872|31.63|\n||Ours|182.5|348|0.183|\n\n# Q8: Diagram Clarity Issue\n>Thank you for the question. We believe the reviewer refers to **Figure 1**. *Composite degradation* does not exclusively indicate simulated data. For example, low-light degradation in VI is real. Importantly, it also refers to the cases where both IR & VI are degraded, and VI suffers from multi-type degradations simultaneously.  (I), (II), and (III) respectively highlight the limitations of existing methods and our advantages under **real-world scenarios**, **composite degradations**, and **varying severity levels**. We will further clarify the definition and relationships in the camera-ready version.\n\n# Q9: Frequency Spectrum Comparison\n>Thank you for the valuable suggestion. We will add the frequency spectrum of clean images in the camera-ready version to better illustrate differences between various degradations.\n\n# Q10: Training Data Dependency\n>Thank you for the insightful question. To investigate the necessity and impact of the proposed DDL-12 dataset, we retrained our model on the **Enhanced Multi-Spectral Various Scenarios (EMS) dataset** [r1], which contains sensor- and illumination-related degradations but lacks the full range of complex degradations modeled in DDL-12.\n\n>**Table r5** presents the quantitative results of the retrained model on real nighttime scenarios from the MSRS dataset and real-world weather degradations from AWMM-100k. On the MSRS dataset, the model trained on EMS shows slight performance drops in CLIP-IQA, MUSIQ, and TRes, and a more notable degradation in the SD metric. This can be attributed to the limited diversity in EMS, which weakens the model’s ability to perceive and adapt to compound degradations and different degradation levels. In addition, the fusion output tends to a uniform brightness distribution, thereby reducing contrast and structural diversity, which leads to a significant decrease in SD.\n\n>On AWMM-100k, the performance drops more significantly across all metrics. This is primarily because EMS does not provide any degradation patterns or priors related to adverse weather conditions, making it difficult for the model to generalize to scenarios involving haze or snow.\n\n>**These findings highlight the importance of DDL-12, which offers diverse and physically grounded synthetic degradations, enabling better learning of degradation-aware representations and significantly improving real-world generalization.**\n\n**Table r5.** Quantitative comparison under real-world degradation scenarios with different training datasets.\n|Real-world Scenarios|Training Datasets|CLIP-IQA|MUSIQ|TReS|SD|\n|:-:|:-:|:-:|:-:|:-:|:-:|\n|**MSRS(Nighttime)**|EMS|0.12|27.49|21.53|40.13|\n||DDL-12(Ours)|0.14|28.46|21.61|52.93|\n|**AWMM-100k(Haze/Snow)**|EMS|0.23|49.16|56.85|33.52|\n||DDL-12(Ours)|0.28|57.51|60.30|44.24|\n\nAs the submission system supports only TeX format, we are unable to upload additional figures. We apologize for this limitation and will incorporate the above updates and results in the main paper or appendix."}, {"review_id": "Nmsxm1uLWq", "reviewer": "Reviewer_iEbx", "summary": "This paper proposes a controllable image fusion network guided by language-vision prompts, named ControlFusion, to address the problem of complex degradations in real-world scenarios. It integrates a physics-driven degradation model, a spatial-frequency collaborative visual adapter (SFVA), and a prompt-modulated restoration and fusion network. The model is capable of handling compound degradations and supports both language and visual prompts for further control. Extensive experiments on multiple datasets demonstrate the model's fusion quality and robustness under various degradation conditions.", "questions": "Although Figure 8 demonstrates the superiority of visual prompts over textual prompts, I would encourage the authors to further clarify: \n\n1. Whether the visual prompt remains robust across different levels of degradation? \n2. Whether the visual prompt is capable of handling compound degradations? Besides, please address the concerns in the Weakness part.", "limitations": "Yes.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strengths\n\n1. The paper introduces an imaging degradation model based on Retinex theory and atmospheric scattering, covering various degradation types for both infrared and visible modalities.\n2. ControlFusion can control the fusion process through textual and visual prompts, enabling the network to flexibly adapt to different types and levels of degradation.\n3. The method uses a spatial-frequency visual adapter (SFVA) to extract visual degradation prompts and align them with CLIP-based text embeddings, which is well-designed and addresses the limitations of purely text-driven methods.\n4. The authors evaluate the model across multiple datasets (MSRS, LLVIP, RoadScene, FMB), multiple metrics (EN, SD, VIF, Qabf, CLIP-IQA, MUSIQ, TReS), and both with/without pre-enhancement. Ablation studies and real-world generalization are also included.\n\n## Weaknesses\n\n### Major Weaknesses\n\n1. Although the visual prompt pathway alleviates dependence on textual input, the prompt-modulated module still hinges on CLIP-based representations. How does this ensure the effective extraction of degradation prompts from the source images?\n2. The model includes multi-stage Transformers, visual-text prompt alignment, and SFVA, which increases inference time and model complexity. The author should compare the parameters and time complexity of the model.\n3. The proposed method supports user-provided text prompts that include degradation severity. However, the paper does not provide sufficient details on the design of the prompts, such as the structure of text templates or how multiple degradations and their combinations are represented.\n4. In the experimental evaluation related to degradations, the dataset used appears to contain a mixture of real and synthetic degradations. Additionally, qualitative results in Figure 5 alone is not sufficiently convincing to demonstrate the method’s effectiveness under real-world scenarios. To better validate effectiveness and robustness, the authors are encouraged to provide both quantitative and qualitative comparisons on datasets only with real degradations, such as night-time scenes from MSRS.\n\n### Minor Weaknesses\n\n1. The notation in Equation (9) needs to be corrected. Specifically, $Q_{vi}$ and $K_{ir}$ appear twice in the equation, which is confusing and likely a typographical error.\n2. Some sentences in the paper have punctuation issues. For example, in Section 5.3, the first sentence: \"removing composite degradations in real scenes..\" contains an incorrect punctuation mark.", "was_revised": true, "final_justification": "The response effectively addressed my concerns, and I have decided to maintain my accept score.", "rebuttal": "# Q1: Effectiveness of Image-Based Degradation Prompts\n> Thank you for the insightful question. In our SFVA and prompt-modulated module, CLIP-based representations act as a bridge between visual and textual prompts, allowing our model to alleviate its dependence on textual input during inference. To ensure that the degradation prompts extracted from source images are effective, we design our SFVA from two perspectives. On the one hand, we incorporate a **frequency-aware branch to extract informative and discriminative cues in the frequency domain**. On the other hand, we introduce MSE loss and cosine similarity loss to **align the visual prompts with their corresponding textual counterparts** during training, thereby improving the consistency and reliability of the visual degradation representations.\n\n> As presented in **Figure 8 (Appendix)**, **using either visual or textual prompts leads to similarly consistent visual results, suggesting that visual degradation prompts from the source images are indeed effective**. To further validate this, we compute the cosine similarity between visual and textual prompts across different degradation types and levels, with results presented in **Tables r1** and **r2**. As shown in the results, our similarity scores are consistently higher than those of OneRestore in all scenarios except for low-light. This improvement primarily stems from the introduction of **our frequency-aware branch**, which enables better perception of degradation types. It is worth emphasizing that OneRestore’s visual prompt branch can only recognize a limited set of degradation types, such as rain, haze, and low-light, as well as their combinations, but lacks the ability to model degradation severity. In contrast, our method not only identifies the degradation types but also captures the corresponding degradation levels. Notably, when degradations become more severe, our degree-level matching becomes increasingly precise,  as shown in **Table r2**, which is consistent with human visual perception.\n\n**Table r1.** Cosine similarity between visual and textual prompts across various degradation types at medium severity.\n|Scenarios|Ours|OneRestore|\n|:-:|:-:|:-:|\n|VI (Rain)|0.899|0.8703|\n|VI (Haze)|0.8862|0.8749|\n|VI (Low-light, LL)|0.8939|0.9016|\n|VI (Over-exposure, OE)|0.9156|0.0289|\n|VI (Random noise, RN)|0.9359|0.0651|\n|IR (Stripe noise, SN)|0.9285|/|\n|IR (Low-contrast, LC)|0.9492|/|\n|VI (Rain&Haze, RH)|0.8516|0.8427|\n|VI (LL&RN)|0.9125|0.0072|\n|VI (OE)&IR(LC)|0.9073|/|\n|VI (RH)&IR(RN)|0.9208|/|\n\n**Table r2.** Cosine similarity between visual and textual prompts under varying degradation levels in the low-light scenario.\n|Degradation Levels|Level 1|Level 4|Level 7|Level 10|\n|:-:|:-:|:-:|:-:|:-:|\n|Similarity|0.8891|0.9147|0.9356|0.9439|\n# Q2: Model Complexity Analysis\n> Thank you for the valuable suggestion. We compare the computational complexity of various methods in **Table r3**, including **parameters, FLOPs, and runtime**. Although **our method is not the most efficient, it achieves acceptable inference efficiency, particularly when compared to joint restoration-fusion methods, such as DRMF, Text-DiFuse, and Text-IF.** Furthermore, **Table r4** presents the overhead introduced by individual components. We observe that **CLIP** and **SFVA**,  as key components for visual-text prompt alignment, significantly improve performance, as shown in **Table 4 (IV)**, where *“w/o PMM”* refers to the model without aligned visual-text prompts. Particularly, **this performance gain is attained with a relatively modest increase in computational overhead.**\n\n**Table r3.** Computational efficiency comparison with pre-enhancement. For fusion methods without restoration, we add the average cost of Instruct-IR and OneRestore.  **Bold**: best; *Italic*: second-best.\n| |Methods|Parm.(M)|Flops(G)|Time(s)|\n|:-:|:-:|:-:|:-:|:-:|\n|**Restoration**|Instruct-IR|16.0|6.7|0.017|\n||OneRestore|6.0|1.3|0.004|\n|**Fusion**|DDFM|563.7|5224|34.51|\n||DRMF|170.9|4575|0.842|\n||EMMA|*12.5*|*45.5*|**0.048**|\n||LRRNet|**11.0**|**18.2**|*0.096*|\n||SegMiF|56.0|358|0.158|\n||Text-IF|89.0|302|0.157|\n||Text-DiFuse|157.4|4872|31.63|\n||Ours|182.5|348|0.183|\n\n**Table r4.** Overhead of individual components.\n|Components|Parm.(M)|Flops(G)|Time(s)|\n|:-:|:-:|:-:|:-:|\n|CLIP-L/14@336px|102.00 (55.88%)|13.088 (**3.77%**)|0.0120 (**6.55%**)|\n|SFVA|15.27 (**8.37%**)|26.095 (**7.51%**)|0.0067 (**3.66%**)|\n|Full Model|182.54|347.57|0.1833|\n\n# Q3: Prompt Design Details\n> We appreciate the reviewer’s valuable suggestion regarding the design details of our textual prompts. Our prompts are designed to guide the fusion network by indicating the **modality** affected, the **degradation type**, and its **severity**. \n\n> A typical prompt template for a **single degradation** is structured as follows: \"We are performing infrared and visible image fusion, where the `modality` image suffers from grade-`severity` `degradation type` problem.\"\n\nHere:\n- **Modality** includes `infrared` or `visible`.\n- **Severity** is an integer from 1 to 10.\n- **Degradation types** include: `rain`, `haze`, `low-light`, `overexposure`, `random noise`, `stripe noise`, `low contrast`, etc.\n\nFor **composite degradations**, we adopt extended templates such as:\n> \"We are performing infrared and visible image fusion. Please handle grade-`severity A` `degradation type A` problem in the `modality A` image, and grade-`severity B` `degradation type B` degradation  in the `modality B` image.\"\n\nor:\n\n> \"We are performing infrared and visible image fusion. Please address level-`severity` `degradation type A` and `degradation type B` degradations in the `modality` image.\"\n\nThese templates are flexibly composed to represent both intra- and inter-modal degradation combinations.\n\nAn instantiation example is:  \n> *\"We are performing infrared and visible image fusion. Please handle the grade-6 low-light degradation in the visible image, and the grade-8 stripe noise interference in the infrared image.\"*\n\n> To cultivate a robust generalization capability, we curated a diverse set of over **100 unique textual formulations** for each task. This prompt rephrasing strategy augments linguistic variability and improves the model’s robustness to diverse prompt styles during inference.\n\n> We will add these illustrations of our prompt templates and examples in the camera-ready version.\n\n# Q4: Real Degradation Evaluation\n> Thank you for the valuable suggestions. To address your concerns, we conducted additional evaluations on real nighttime scenes from the MSRS dataset. Due to the system’s text-only submission constraint, we regret that qualitative results cannot be presented here. Quantitative results are shown in **Table r5**. Our method achieves the best performance on **CLIP-IQA**, **TRes**, and **SD** metrics, while slightly trailing **Text-DiFuse** on the **MUSIQ** metric. \n\n> Furthermore, we conducted quantitative comparisons on the real weather-degraded benchmark **AWMM-100k** [r1], with the results shown in **Table r6**. Our method outperforms others across all metrics under real-world weather degradation conditions. Additionally, the corresponding qualitative results also demonstrate the clear advantages of our approach.\n\n> These experimental results collectively validate the effectiveness and robustness of our method in real-world scenarios.\n\n**Table r5.** Quantitative results on nighttime scenes from MSRS.\n|Methods|CLIP-IQA|MUSIQ|TReS|SD|\n|:-:|:-:|:-:|:-:|:-:|\n|DDFM|0.10|25.03|19.40|33.94|\n|DRMF|0.10|27.52|16.05|46.72|\n|EMMA|0.09|26.71|18.51|42.66|\n|LRRNet|0.10|27.04|16.91|42.69|\n|SegMiF|0.09|27.40|*21.08*|44.31|\n|Text-IF|0.10|28.16|20.64|*49.75*|\n|Text-DiFuse|*0.12*|**28.62**|18.97|43.73|\n|Ours|**0.14**|*28.46*|**21.61**|**52.93**|\n\n**Table r6.** Quantitative results on real-world weather degradations from AWMM-100k.\n|Methods|CLIP-IQA|MUSIQ|TReS|SD|\n|:-:|:-:|:-:|:-:|:-:|\n|DDFM|0.23|41.92|53.86|36.01|\n|DRMF|0.23|50.05|56.67|39.73|\n|EMMA|0.25|47.16|49.72|40.40|\n|LRRNet|0.19|46.75|50.71|37.19|\n|SegMiF|*0.26*|*50.71*|54.31|33.31|\n|Text-IF|0.21|45.63|51.75|32.09|\n|Text-DiFuse|0.23|46.90|*57.71*|*41.53*|\n|Ours|**0.28**|**57.51**|**60.30**|**44.24**|\n\n[r1] Li, Xilai, et al. All-weather multi-modality image fusion: Unified framework and 100k benchmark. arXiv preprint arXiv:2402.02090, 2024.\n\n# Q5: Writing and Notation Clarity\n> Thank you for pointing out the notation and punctuation issues. We will carefully revise the manuscript and fix these issues in the camera-ready version.\n\nAs the submission system supports only TeX format, we are unable to upload additional figures. We apologize for this limitation and will incorporate the above updates and results in the main paper or appendix."}, {"review_id": "ZwgwBaI2z3", "reviewer": "Reviewer_SAs2", "summary": "This paper introduces ControlFusion, a novel and controllable framework for infrared-visible image fusion (IVIF) that simultaneously performs image restoration and fusion. The core problem is the poor performance of existing methods on real-world images suffering from complex, composite degradations (e.g., noise, low-light, haze, blur) and their lack of flexibility to accommodate user-specific needs. The authors presents a physics-driven degradation model to construct a realistic training dataset (DDL-12), and then use it for training controllable image fusion network. The training stage involves textual-visual prompt alignment and prompt-modulated restoration & fusion to achieve the controllable image fusion with prompts.", "questions": "Please see the weaknesses.", "limitations": "Yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n\n1. The paper addresses a critical and practical limitation of current image fusion techniques: the inability to handle complex, real-world degradations and the lack of user control. The core idea of using language-vision prompts to control the fusion process is interesting and effectively implemented.\n\n2. The paper proposes a realistic imaging model based on physical principles like the Retinex theory and atmospheric scattering to synthesize a high-quality training dataset (DDL-12) that bridges the domain gap between synthetic and real-world data.\n\n3. Extensive experiments demonstrate that ControlFusion significantly outperforms state-of-the-art methods in both fusion quality and degradation handling, while also providing user-driven customizability.\n\n\nWeaknesses\n\n1. The introduction of multiple sophisticated modules for CLIP, SFVA, and PMM inevitably increases the model's parameter count and computational overhead compared to simpler fusion networks. This could be a potential drawback for deployment on resource-constrained edge devices where real-time performance is critical. This naturally leads to a question about the performance-efficiency trade-off. While the performance improvement is significant, it is important to consider whether this improvement mainly comes from using more computatioal cost.\n\n2. The paper's SFVA extracts features from two fundamentally different domains: spatial and frequency. The method to combine them is a simple concatenation followed by a linear layer. This leads to a question about its effectiveness, as spatial features (capturing local textures) and frequency features (representing global periodic patterns) have vastly different properties. A simple linear fusion might not be powerful enough to model their different characteristics. A comprehensive study on this (e.g., more sophisticated fusion mechanisms) may be necessary to strengthen the paper's contribution.\n\n3. While the DDL-12 dataset is a synthetic dataset, the model trained with it may not be generalizable to real-world scenarios. A \"domain gap\" inevitably exists between these simulated degradations and the unpredictable and complex degradations found in real-captured images. To fully validate the model's practical utility and its superiority in bridging the domain gap, it would be beneficial to include more experiments on realistic benchmarks and real-cpatured images.", "was_revised": true, "final_justification": "Thank you to the authors for their efforts and detailed responses. The responses address most of my concerns. After reviewing the clarifications and peer reviewers' comments, I believe the paper meets the acceptance criteria. Therefore, I’ve decided to Accept.", "rebuttal": "# Q1: Computational Efficiency\n> Thanks for the thoughtful comment. In response, we compared the computational cost of different fusion methods with pre-enhancement in **Table r1**, including parameters, FLOPs, and average runtime. Although **our method is not the most efficient, it achieves acceptable inference efficiency, especially when compared with joint restoration-fusion methods such as Text-DiFuse, DRMF, and Text-IF.**\n\n> Furthermore, **Table r2** reports the computational overhead of individual components. Since we use the pretrained CLIP model, its parameters do not incur additional training costs, and its FLOPs and runtime remain within acceptable bounds. To reduce complexity, our model also provides a lightweight SFVA as the prompt branch, significantly reducing both parameters and runtime. In addition, the PMM module consists of only a few MLP layers and simple modulation operations, incurring negligible overhead, particularly in terms of FLOPs and runtime. \n\n> In summary, **our method improves fusion performance without imposing a significant computational burden**. The **computational overhead introduced by the core components**, including CLIP, SFVA, and PMM, in terms of FLOPs and runtime, **remains within a practically acceptable range**.\n\n**Table r1.** Computational efficiency comparison with pre-enhancement. For fusion methods without restoration, we add the average cost of Instruct-IR and OneRestore. **Bold**: best; *Italic*: second-best.\n| |Methods|Parm.(M)|Flops(G)|Time(s)|\n|:-:|:-:|:-:|:-:|:-:|\n|**Restoration**|Instruct-IR|16.0|6.7|0.017|\n||OneRestore|6.0|1.3|0.004|\n|**Fusion**|DDFM|563.7|5224|34.51|\n||DRMF|170.9|4575|1.979|\n||EMMA|*12.5*|*45.5*|**0.048**|\n||LRRNet|**11.0**|**18.2**|*0.096*|\n||SegMiF|56.0|358|0.158|\n||Text-IF|89.0|302|0.157|\n||Text-DiFuse|157.4|4872|31.63|\n||Ours|182.5|348|0.183|\n\n**Table r2.** Computational overhead of individual components. Values in parentheses indicate the percentage relative to the full model.\n|Components|Parm.(M)|Flops(G)|Time(s)|\n|:-:|:-:|:-:|:-:|\n|CLIP-L/14@336px|102.00 (55.88%)|13.088 (**3.77%**)|0.0120 (**6.55%**)|\n|SFVA|15.27 (**8.37%**)|26.095 (**7.51%**)|0.0067 (**3.66%**)|\n|PMM|6.94 (**3.80%**)|0.014 (**0.004%**)|0.0001 (**0.05%**)|\n|Full Model|182.54|347.57|0.1833|\n\n# Q2: Spatial-Frequency Fusion Mechanisms\n> Thank you for your insightful comment regarding the fusion mechanism adopted in our SFVA module for integrating spatial and frequency features. To address this concern, we conducted additional experiments by introducing two more sophisticated fusion mechanisms: **1) Weighted Fusion:** We first enhance the extracted spatial and frequency features using ResBlocks, and then learn adaptive fusion weights for feature aggregation. **2) Attention Fusion:** Inspired by SwinFusion [r1], we incorporate a cross-attention mechanism to facilitate more effective interaction and better integration between spatial and frequency features.\n\n> We first evaluated these fusion mechanisms by comparing the cosine similarity between the visual prompts and their corresponding textual prompts across various degradation types and severity levels. The results, shown in **Tables r3** and **r4**, indicate that the more sophisticated fusion strategies lead to better alignment between visual and textual prompts, suggesting improved degradation characterization.\n\n> It is worth noting that, as shown in **Table r5**, these improvements come at the cost of **significantly increased computational complexity**. Both the weighted and attention fusion mechanisms **introduce more than 50% additional parameters, FLOPs, and runtime** compared to the simple concatenation strategy. However, **the actual gains in fusion performance brought  by these more complex mechanisms are marginal.**\n\n> Although advanced fusion mechanisms offer some benefits, our current design strikes a practical balance between effectiveness and computational efficiency. **We sincerely appreciate the reviewer’s suggestion and will explore lightweight yet effective spatial–frequency fusion strategies in future work.**\n\n**Table r3.** Cosine similarity between visual and textual prompts across various degradation types at medium severity across different fusion mechanisms.\n|Scenarios|Weighted Fusion|Attention Fusion|Ours|\n|:-:|:-:|:-:|:-:|\n|VI(Rain)|0.9137|**0.9172**|0.899|\n|VI(Haze)|0.8904|**0.8927**|0.8862|\n|VI(Low-light)|0.8991|**0.9005**|0.8939|\n|VI(Over-exposure, OE)|**0.9216**|0.918|0.9156|\n|VI(Random noise, RN)|**0.9387**|0.9362|0.9359|\n|IR(Stripe noise, SN)|**0.9302**|0.9293|0.9285|\n|IR(Low-contrast, LC)|**0.9511**|0.9507|0.9492|\n|VI(LL&RN)|0.9129|**0.9131**|0.9125|\n|VI(OE)&IR(LC)|**0.9184**|0.9123|0.9073|\n|VI(RH)&IR(RN)|**0.9351**|0.9275|0.9208|\n\n**Table r4.** Cosine similarity between visual and textual prompts under varying degradation levels in the low-light scenario across different fusion mechanisms.\n|Degradation Level|Weighted Fusion|Attention Fusion|Ours|\n|:-:|:-:|:-:|:-:|\n|Level 1|**0.909**|0.8951|0.8891|\n|Level 4|0.9296|**0.931**|0.9147|\n|Level 7|0.9416|**0.9458**|0.9356|\n|Level 10|0.9511|**0.9585**|0.9439|\n\n**Table r5.** Comparison of fusion performance and computational efficiency among different fusion mechanisms in SFVA.\n| |Weighted Fusion|Attention Fusion|Ours|\n|:-:|:-:|:-:|:-:|\n|**Computational Efficiency**| | | |\n|Parm.(M)|26.925 (+76.3%)|24.72 (+61.8%)|**15.274**|\n|Flops(G)|52.26 (+100.3%)|51.781 (+98.4%)|**26.095**|\n|Time(s)|0.0103 (+53.7%)|0.0098 (+46.3%)|**0.0067**|\n|**VI(LL&RN)**| | | |\n|CLIP-IQA|**0.227 (+0.89%)**|0.224|0.225|\n|MUSIQ|49.294|**49.403 (+0.14%)**|49.333|\n|TReS|**49.604 (+0.18%)**|49.595|49.513|\n|EN|7.132|**7.138 (+0.38%)**|7.111|\n|**VI(OE)&IR(LC)**| | | |\n|CLIP-IQA|0.186|0.186|**0.187**|\n|MUSIQ|**50.513(+0.07%)**|50.507|50.479|\n|TReS|50.327|**50.373 (+0.15%)**|50.298|\n|SD|50.873|50.938|**50.955**|\n|**VI(RH)&IR(RN)**| | | |\n|CLIP-IQA|0.179|**0.181 (+1.12%)**|0.179|\n|MUSIQ|50.121|**50.204 (+0.19%)**|50.107|\n|TReS|**51.129 (+0.07%)**|50.953|51.091|\n|SD|55.614|**55.71 (+0.53%)**|55.417|\n\n[r1] Ma J, Tang L, Fan F, et al. SwinFusion: Cross-domain long-range learning for general image fusion via swin transformer[J]. IEEE/CAA JAS, 2022, 9(7): 1200-1217.\n\n# Q3: Generalization to Real-world Scenarios\n> Thank you for the valuable suggestion. Although our model is trained solely on the synthetic DDL-12 dataset, it generalizes well to real-world scenarios, as evidenced by the qualitative results in **Figure 5** and **Figure 10 (Appendix)**. \n\n> To demonstrate the practical utility of our method and its effectiveness in bridging the domain gap, we conducted evaluations on real nighttime scenarios from the MSRS dataset and a real weather-degraded benchmark **AWMM-100k** [r2], as shown in **Tables r6** and **r7**, respectively. The MSRS dataset suffers from low-light and noise degradations, while AWMM-100k contains severe haze and snow conditions. As shown in the results, our method achieves clear advantages on these real-world benchmarks. This indicates that **the proposed synthetic degradation scheme, grounded in physical imaging models, effectively reduces the domain gap between simulated and real data, thereby improving the model's real-world generalization and applicability**.\n\n**Table r6.** Quantitative results on nighttime scenes from MSRS.\n|Methods|CLIP-IQA|MUSIQ|TReS|SD|\n|:-:|:-:|:-:|:-:|:-:|\n|DDFM|0.10|25.03|19.40|33.94|\n|DRMF|0.10|27.52|16.05|46.72|\n|EMMA|0.09|26.71|18.51|42.66|\n|LRRNet|0.10|27.04|16.91|42.69|\n|SegMiF|0.09|27.40|*21.08*|44.31|\n|Text-IF|0.10|28.16|20.64|*49.75*|\n|Text-DiFuse|*0.12*|**28.62**|18.97|43.73|\n|Ours|**0.14**|*28.46*|**21.61**|**52.93**|\n\n**Table r7.** Quantitative results on real-world weather degradations from AWMM-100k.\n|Methods|CLIP-IQA|MUSIQ|TReS|SD|\n|:-:|:-:|:-:|:-:|:-:|\n|DDFM|0.23|41.92|53.86|36.01|\n|DRMF|0.23|50.05|56.67|39.73|\n|EMMA|0.25|47.16|49.72|40.40|\n|LRRNet|0.19|46.75|50.71|37.19|\n|SegMiF|*0.26*|*50.71*|54.31|33.31|\n|Text-IF|0.21|45.63|51.75|32.09|\n|Text-DiFuse|0.23|46.90|*57.71*|*41.53*|\n|Ours|**0.28**|**57.51**|**60.30**|**44.24**|\n\n[r2] Li X, Liu W, Li X, et al. All-weather multi-modality image fusion: Unified framework and 100k benchmark. *arXiv preprint arXiv:2402.02090* (2024)."}]

Author rebuttal:

Meta-review: This work proposes a novel framework for image fusion designed to handle complex, multi-stage degradations in real-world scenarios. The paper's core contribution is a controllable fusion process guided by a language-vision prompt mechanism, which allows for automated degradation perception and fine-grained user control.

The reviewers unanimously acknowledged the novelty of the methodology and the significance of the problem being addressed. They initially raised several valid and critical concerns, primarily centered on the model's generalization from synthetic data to real-world conditions, its computational complexity, and the need for more rigorous validation. In response, the authors provided a comprehensive rebuttal. They made substantial efforts to address the reviewers' concerns, most notably by performing extensive experiments on challenging real-world benchmarks. These new results convincingly demonstrated the method's practical effectiveness and its ability to bridge the synthetic-to-real domain gap. lt is strongly recommended to incorporate the generalization experiments on real-world benchmarks and computational efficiency analysis into the camera-ready version.

The reviewers feel satisfied with the rebuttal, and the final scores are all positive: "Borderline accept", "Accept", "Accept", and "Accept". Therefore, the decision is to Accept this paper. Additionally, given its technical novelty, and its potential to influence research in areas such as multi-modal learning, image enhancement under complex degradations, and controllable image generation, the AC recommends this paper for an oral presentation.
Decision: Accept (oral)