Task-Gated Multi-Expert Collaboration Network for Degraded Multi-Modal Image Fusion
Authors: Yiming Sun
Xin Li
Pengfei Zhu
Qinghua Hu
Dongwei Ren
Huiying Xu
Xinzhong Zhu
Venue: ICML 2025 poster (2025)
Keywords: Degraded Multi-modal Image Fusion
Task-aware Gating
Multi-expert Collaboration
Primary area: applications->computer_vision
TL;DR: A task-gated multi-expert collaboration network for all-in-one multi-modal image restoration and fusion.

Abstract: Multi-modal image fusion aims to integrate complementary information from different modalities to enhance perceptual capabilities in applications such as rescue and security. However, real-world imaging often suffers from degradation issues, such as noise, blur, and haze in visible imaging, as well as stripe noise in infrared imaging, which significantly degrades model performance. To address these challenges, we propose a task-gated multi-expert collaboration network (TG-ECNet) for degraded multi-modal image fusion. The core of our model lies in the task-aware gating and multi-expert collaborative framework, where the task-aware gating operates in two stages: degradation-aware gating dynamically allocates expert groups for restoration based on degradation types, and fusion-aware gating guides feature integration across modalities to balance information retention between fusion and restoration tasks. To achieve this, we design a two-stage training strategy that unifies the learning of restoration and fusion tasks. This strategy resolves the inherent conflict in information processing between the two tasks, enabling all-in-one multi-modal image restoration and fusion. Experimental results demonstrate that TG-ECNet significantly enhances fusion performance under diverse complex degradation conditions and improves robustness in downstream applications. The code is available at https://github.com/LeeX54946/TG-ECNet.

Task-Gated Multi-Expert Collaboration Network for Degraded Multi-Modal Image Fusion
Yiming Sun
Xin Li
Pengfei Zhu'
Qinghua Hu 234
Dongwei Ren 234
Huiying Xu Xinzhong Zhu?
Abstract
Multi-modal image fusion aims integrate com- plementary information from different modali- ties to enhance perceptual capabilities in appli- cations such as rescue and security. However, real-world imaging often suffers from degrada- tion issues, such as noise, blur, and haze in vis- ible imaging, as well as stripe noise in infrared imaging, which significantly degrades model per- formance. To address these challenges, pro- pose task-gated multi-expert collaboration net- work (TG-ECNet) for degraded multi-modal im- age fusion. The core our model lies in the task-aware gating and multi-expert collaborative framework, where the task-aware gating oper- in two stages: degradation-aware gating dy- namically allocates expert groups for restoration based degradation types, and fusion-aware gat- ing guides feature integration across modalities to balance information retention between fusion and restoration tasks. To achieve this, we de- sign two-stage training strategy that unifies the learning of restoration and fusion tasks. This strategy resolves the inherent conflict in infor- mation processing between the two tasks, en- abling all-in-one multi-modal image restoration and fusion. Experimental results demonstrate that TG-ECNet significantly enhances fusion perfor- mance under diverse complex degradation con- ditions and improves robustness in downstream applications. The code is available https: httgghtub.coooommmm wtt.ccooeeeeee///e//eee
Degraded multi-modal image
DRMF (ACM MM 2024)
Text-1F (CVPR 2024)
Fused image (Ours)
Figure I. Comparison of degraded multi-modal image fusion. For multi-degraded multi-modas images, the proposed method shows superior restoration and fusion quality compared to the SOTA methods DRMF (Tang al., 2024) and Text-IF (Yi et al., 2024), especially in the highlighted regions, where our method better preserves details and reduces noise.
1. Introduction
Multi-modal image fusion plays pivotal role in applica- tions (Sun et al., 2022a; Liu et al., 2024) such as rescue, security, and surveillance by integrating complementary information from diverse imaging modalities (Ma et al., 2019a; Zhang et al., 2021; Xu et al., 2022a; Liu et al., 2021; 2022b). The visible modality provides superior textural details, while the infrared modality effectively highlights thermal targets. The combination of visible and infrared imaging advantages enables the synthesis of fused images suitable for all-day operation (Sun et al., 2022b; Liu et al., 2023; Zhao et al., 2023b; Cao et al., 2023; Liu et al., 2020), thereby enhancing application robustness complex envi- ronments.
ECNet.
School of Automation, Southeast University, Nanjing, China College Intelligence and Computing, Tianjin University, Tian- jin, China 'Engineering Research Center of City Intelligence and Digital Governance, Ministry of Education of the People's Repub- lie of China, Tianjin, China 'Haihe Lab of ITAI, Tianjin, China "Scoool of Computer Science and Technology. Zhejiang Normal University, Jinhua, China. Correspondence to: Pengfei Zhu <zhu- crngfci@rju.cdu.cm
However, real-world multi-modal imaging systems are sus- ceptible to performance degradation caused by multiple in- terfering factors (Tang et al., 2022b; 2023; Sun et al., 2024; Tang et al., 2024: Yi et al., 2024). Specifically, the key com- ponent for capturing thermal information infrared sensors is the focal plane array, whose variations the bias voltage of the readout circuit in different columns often result in
Proceedings of the 4dnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Task-Gated Multi-Expert Collaboration Network for Degraded Multi-Modal Image Fusion
alternating bright and dark stripe noise in the infrared image. Visible imaging is also susceptible to mixed degradation by noise, blur, haze, and other disturbances. These degra- dations seriously weaken the visual quality of modll images (as shown in Fig. 1); leading to a degradation in the performance of the fused images and affecting their robust- ness and effectiveness in critical downstream applications.
restoration and fusion. The main contributions of this paper are summarized as follows:
We propose unified framework for degraded multi- modal image restoration and fusion, which bridges different tasks together through a two-stage training strategy to learn inter-task information while avoiding mutual interference, enabling all-in-one processing.
To address the problem of degraded multi-modal image fu- sion, a direct approach is cascade two tasks sequentially: first, restore the degraded images, and then fusion the re- stored multi-modal images. However, this strategy presents dual limitations. On one hand, the diversity of degradation types necessitates pre-storing a large number of restoration models for multiple degradation conditions, entailing pro- hibitive costs when addressing complex scenarios (Tang et al., 2022b; 2023; Sun et al., 2024). On the other hand, the inherent disconnection between these two tasks leads to performance degradation: restoration operations aiming to restore degraded information may inadvertently weaken features beneficial for fusion, while fusion processes risk propagating restoration failures into the final results. Cur- rent state-of-the-art methods, such as DRMF (Tang et al., 2024) and Text-IF (Yi et al., 2024), attempt to address these limitations by integrating restoration and fusion into uni- fied framework through diffusion models or text-guided mechanisms. Nevertheless, as illustrated in Fig. 1, these approaches demonstrate insufficient capability in handling complex multi-degradation scenarios, yielding suboptimal fusion outcomes that hinder practical applications in real- world environments with intricate degradation conditions.
We propose the task- gating and multi-expert collaboration module. The degradation-aware gating adapts to different degradation types and selects the optimal expert group for image restoration, while the fusion-aware gating dynamically balances the informa- tion retention between fusion and restoration tasks to achieve better fusion performance.
We construct a large-scale degraded multi-modal im- age fusion benchmark, DeMMI-RF, which contains more than 30, 000 multi-modal data of different degra- dation types, including those from UAVs and driving viewpoints. Results on multiple datasets validate the su- perior performance of the model in complex degraded scenarios and robustness for downstream applications.
2. Related Work
2.1. Multi-modal Image Fusion
Multi-modal image fusion integrates complementary infor- mation from various modalities (e-g.. visible and infrared) to produce enriched representations (Ma et al., 2019;; Liu et al., 2024). Deep learning has driven progress in this field, with CNNs (Zhang et al., 2020; Wang et al., 2022a; Sun et al., 2022b; Xu et al., 2022b) and GANs (Liu et al., 2022a; Ma et al., 2019b) learning fusion rules directly from the data. Recent innovations, such as attention mechanisms and transformer- based architectures (Tang al., 2022c; Wang et al., 2022b), enhance performance by modeling long-range dependencies and modality features. Frameworks like MGDN (Guan et al., 2023) further unify sub-tasks within fusion. However, most existing deep learning meth- ods focus solely on fusion and neglect the impact of degra- dations, such as noise, blur, and haze, which are common in worldworld scenarios. This limitation significantly reduces their robustness and applicability in practical applications.
In this work, we propose the task-gated multi-expert collab- oration network (TG-ECNet) that unifies degraded multi- modal image restoration and fusion into a common frame- work. Specifically, we propose task-aware gating modules for the degradation and fusion stages, respectively. Among them, degradation-awan aware gating ensures robust restoration based on input features by adaptively learning the degrada- tion types (e.g., noise, blur, haze, streak noise) and select- ing the most appropriate processing paths with group of experts. Meanwhile, the fusion-aware gating selectively ag- gregates modai features and selects the most valuable complementary information for high-quality fusion by multi- expert collaboration. TG-ECNet adopts a two-stage training strategy to bridge the image restoration and fusion tasks, which ensures balanced optimization of the restoration and fusion objectives. By decoupling the learning process, the two-stage strategy minimizes the interference between the tasks and ultimately achieves all-in-one multi-modal image restoration and fusion. This unified approach not only im- proves the fusion quality but also enhances the adaptability of the model to downstream applications in various com- plex degradation scenarios. In addition, we also construct a large-scale benchmark, DeMMI-RE, for degraded image
2.2. Degraded Image Restoration
Degraded image restoration aims recover high-quality images from degraded inputs, addressing issues like noise, blur, and haze. While traditional approaches (Xia et al., 2023) target individual degradations, real-world scenarios often involve intertwined degradations. All-in-one image restoration aims to address diverse degradation types using a unified model. Recent advances leverage specific task
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Task-Gated Multi-Expert Collaboration Network for Degraded Multi-Modal Image Fusion
Train Stage
ly
Train Stage 2
Degradation- Aware Gating
Degradation-Aware Encoder
Image Restoration
stagel
Decoder
Clean Image
Tv
Mulli-Expert Colloborative
Transformne Block
Transformer Block
Degraded Image
V
Fasien-Aware Goting
U-shaeed Connection
a
Transfomee
Cion
Callaborative
Degradatien-
DegradationAAwe Encoder
Image Restoration Decoder
Fused Image Ip
Gating
Estagez I
Patch Embeeking
Multi-Expe Expli- Collaborative
TransSocuner Dlock
staget
Ckan Image 16 &
Clean Image
1
Dograded Imaae 19
Tmustormer Block
U-haped Connection
Figure 2. The architecture of Tet TG-ECNet consists of a U-shape Transformer framework, a degradation-awarr gating, a fusion-aware gating, and corresponding multi-expert collaboration framework.
learning (Zamir et al., 2022) or degradation-awann mech- anisms (Li et al., 2022; Potlapalli et al., 2023; Cui et al., 2025) to dynamically adapt to varying degradation types without requiring prior knowledge of the degradation. Most existing methods are limited to single-modal images or ad- dress restoration and fusion separately, failing to handle multi-modal data effectively. While recent works (Tang et al., 2024; Li et al., 2024a) tackle quality issues (e.g., low light, haze, and noise) in multi-modal fusion, they re- main restricted to specific degradations. Some approaches, such as Text-IF (Yi et al., 2024) and Text-DiFuse (Zhang al., 2024), employ text-guided restoration but require prior degradation knowledge, while others (e.g., AWFu- sion (Li et al., 2024b)) are confined to weather-related sce- narios. Methods like (Tang et al., 2025) suffer from complex architectures and limited datasets, hindering optimal per- formance. In contrast, our TG-ECNet introduces unified framework that jointly optimizes restoration and fusion, ensuring robust and high-quality fusion real-world sce- narios.
learning of the restoration and fusion tasks.
The architecture of TG-ECNet is shown in Fig. 2. We send pair of degraded infrared image 14 E HR x1 and de- graded visible image I E = into the patch embed- ding and degradation-aware gating to extract the features. Then these features are fed into the degradation-awanl en- coder and image restoration decoder to get I, and Tv. The structure of the encoders and decoders follows (Zamir et al., 2022). It should be noted that the infrared and visible modal branches share encoder and decoder weights. In the second stage, the features F, and Fv from the decoder are also fed into the fusion branch obtain Ir. The overall structure of our model can be notated as:
Ip TGG-ECNNN(T 14).
(1)
3.2. Task-Aware Gating and Multi-Expert Collaborative
As shown in Fig 3, the task-aware gating and multi-cxpert collaborative is the core component of TG-ECNet, integrat- ing two key mechanisms to facilitate adaptive restoration and fusion. The first mechanism, degradation-awar gating, is implemented in the U-shape Transformer encoder to dy- namically identify degradation types present in the input images, such as noise, blur, haze in visible images, and stripe noise in infrared images. Based on the degradation types, task-specific prompts are generated to guide the se- lection of appropriate processing pathways, allowing the model to adaptively restore images tailored to their degra- dation types. The second mechanism, fusion-aware gating, is applied in the image fusion stage and focuses on selec- tively aggregating features from multiple modalities, such as visible and infrared images. By weighting the contri- butions from each modality based on their relevance and complementarity, fusion-aware gating ensures that the most
3. Methods
3.1. Overall Architecture
In this work, we propose the task-gated multi-expert col- laboration network (TG-ECNet) to address the challenges of image quality degradation in multi-modal image fu- sion through all-in-one approach. The framework con- tains: the U-shape Transformer (Zamir et al., 2022) for feature extraction and image decoding, a degradation-awar gated multi-exper collaborative module for all-in-one image restoration, and fusion-aware gated multi-modal collab- oration module for adaptive image fusion. Additionally, a two-stage training strategy is employed to balance the
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Task-Gated Mulei-Expert Collaboration Network for Degraded Multi-Modal Image Fusion
of different modal features: FM = a Fy + B Fr. The fusion-aware gating mechanism F (FM) is applied to the decoder to enhance the fusion process by focusing on the most relevant features from both modalities. This gating mechanism enables the model to selectively weight the con- tributions of each modality, ensuring that the most important information from both images is retained in the fused result. The model then uses the multi-expert collaborative network (Efu, ... Ef to combine features from both visible and infrared images.
Task-Aware Gating
Prompt Components
GAP
Conv
Transformer Block
SoftMax
Task-Amare Wrighting
Ronter
Expert
Expert 2
:
Eopuc
Expert N-1
N
Expert N
OutputFus Σ GFus(FMie (Fx). (3)
Multi-Expert Collaborative
Figure 3. Task-aware gating and multi-expert collaborative.
This collaborative fusion approach ensures that features from both modalities are effectively integrated, preserving critical details from both sources. The final fused image Lf is obtained by merging these features, and a loss is calcu- lated by comparing the fused image with the clean ground truth I5 and Iÿ. This loss is used to refine the model's fusion capabilities, ensuring high-quality fusion results that combine the best features of both input modalities.
informative features are combined, resulting in high-quality fusion output. This mechanism ensures effective feature in- tegration, enabling robust multi-modal fusion that preserves essential information from both input sources.
Degradation-Aware Gated Image Restoration. In the first training stage, TG-ECNet restores degraded images, begin- ning with degraded image 1 or Ty- The model applies the degradation-awwrre gating mechanism G Degrad (1&) to adjust based on the specific degradation present in the input image. This gating enables the model to dynami- cally adapt to different degradation types, ensuring that the appropriate processing strategy is applied. The in- put image is then processed by the degradation-aware coder, which utilizes a multi-expert collaborative network [EPegrad
3.3. Training Strategy and Loss Function
To balance restoration and fusion tasks and minimize inter- ference during optimization, we employ two-stage training strategy. The model uses combination of loss functions during training. In Stage (Restoration- the network is first trained to address degradations in indi- vidual modalities, using loss functions tailored to specific restoration tasks (c.g., denoising, deblurring). The restora- tion loss in Stage is caleulated by comparing the restored images Tv and 7, with their respective clean ground truth images Iú and 1f. In Stage 2, the fusion loss is computed by comparing the fused image Lp with the clean fused ground truth image. The formulation of the loss function( L Degrad grad
training),
..... E Degerdd }, including transformer block, to extract and refine features that are specific to the encoun- tered degradation types. The multi-expert mechanism se- lects the most relevant expert to learn different image fea- tures, optimizing the restoration process. When processing the visible image, the formalization is as follows,
and DDegred) follows (Cao et al., 2023).
Output Σ (1gg E
Degrad (1)).
Lres || T8 + 1| -
(4)
Estagel = Lres + Drag + Lood
Degrad (5)
After feature extraction, the decoder, composed of trans- former blocks, generates a restored version of the image Iv. which is compared with the clean ground truth image Iú to compute the loss, guiding the model to improve restoration accuracy. The same restoration process is applied to the infrared degraded image I4, generating a restored infrared image 1T.
In Stage 2 (Fusion-focuus focused training), after restoration, the network is fine-tuned for fusion tasks. The final loss for the entire network is the sum of the restoration losses from Stage and the fusion loss from Stage allowing the model to simultancously optimize both tasks. By decoupling the learning processes, the strategy ensures that restoration and fusion tasks do not interfere with each other, ultimately en- abling all oll-in-one image restoration and fusion. The formu- lation of the fusion loss Saaettin(Cpizet.. Lgrads and ( follows (Cao al., 2023).
Fusion-Aware Gated Image Fusion. In the second training stage, TG-ECNet focuses on fusing the restored visible and infrared images to create high-quality fused output. The inputs to this stage are the restored visible feature Fv and infrared feature Fj from the previous stage. We introduce learnable weight parameters a and / to guide the fusion
Estage2 = Epizel + Lgrad + Pus.
(6)

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Task-Gated Multi-Exper Collaboration Network for Degraded Multi-Modal Image Fusion
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Figure Qualitative comparisons various methods.
4. Experiment
data augmentation performed by flipping the images horizontally vertically expand the training sample size. We conduct experiments on both our constructed dataset and the EMS dataset (Yi et al., 2024). We trained a single model under 6 degradation settings. The first stage training pro- cess lasted for 30 epochs, and model was directly tested across multiple restoration tasks. The second stage training process lasted for 30 epochs, and the model was directly tested across multiple restoration and fusion tasks. In exper- iments, the number of experts N and the number of selected experts K were heuristically set to 11 and 6, respectively.
4.1. Experimental Setting
Implementation Details. All experiments in this paper were performed 6 NVIDIA GeForce RTX 4090 GPUs, and the model was implemented using the PyTorch 1.12.0 framework. During the training phase, used the Adam optimizer to optimize the network, setting the initial learning rate to 1.0 10-4 and adjusting using the cosine annealing strategy. addition, we randomly cropped the images to a size of 128 x 128 pixels for training. each small batch,
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Task-Gated Multi-Expert Collaboration Network for Degraded Multi-Modal Image Fusion
Relevant analysis can be found in Appendix A.3.
TG-ECNet consistently achieves superior denoising perfor-
mance and structural preservation.
Details of the Constructed Dataset. Existing infrared- visible image datasets rarely take complex degradation sce- narios into account, and there are no multimodal degra- dation datasets related to low-altitude drone perception scenarios. Therefore, we constructed a multimodal multi- degradation image dataset, DeMMI-RF, involving urban street view perspectives and low-altitude drone perspectives. Our DeMMI-RF dataset includes 6 types of degradation: high/medium/loww levels of Gaussian noise, haze, defocus blur, and striped noise. Typical cases of the dataset are shown in Fig. 4, which includes both ground and drone see- narios. DeMMI-RF has 26631 training datasets and 9895 testing datasets, providing a powerful benchmark for de- graded image fusion.
Degraded Visible Images with Noise. Following standard image restoration protocols, we evaluated performance un- der medium, high, and extreme Gaussian noise conditions. For moderate noise (Fig. 5, first row), TG-ECNet effectively removes noise while preserving structural details, outper- forming SeAFusion and EMMA in clarity and information retention. Under high noise (second row), most methods fail to completely eliminate noise, significantly degrading fusion quality. In extreme conditions (third row), while AWFusion and DRMP exhibit excessive blurring and CDDFuse retains noise artifacts, TG-ECNet maintains superior noise suppres- sion and detail preservation, achieving optimal information integration from both modalities.
Competing Methods. To comprehensively evaluate the performance of our proposed framework, we conducted ex- periments on three typical image restoration tasks: image dehazing, denoising, and deblurring. We compared our method with three ofate-of-the-arr art models that jointly ad- dress image restoration and fusion: AWFusion (Li et al., 2024b), DRMF (Tang et al., 2024), and Text-IF (Yi al., 2024). These comparisons were designed to assess the gen- eralization ability of our model in handling multiple types of degradations. In addition, we selected six image fusion modelssDenseFFuse (Li & Wu, 2018). SwinFuse (Wang et al., 2022b), CDDFuse (Zhao et al., 2023a), ScAFusion (Tang et al., 2022a), MGDN (Guan et al., 2023) and EMMA (Zhao et al., 2024) ) for further evaluation. Since these models lack inherent image restoration capabilities, we first pre- processed the degraded images using the AdaIR model (Cui et al., 2025), an all-in-one image restoration framework, to obtain restored images. The restored images were then fed into the aforementioned fusion models.
Degraded Visible Images with Haze. In this experiment, we employ novel atmospheric scattering model that in- herently diminishes image brightness and contrast. The degradation model produces images with reduced bright- ness, lower contrast, and slight color distortions, approxi- mating real-world foggy scenes (see A.1). Infrared images, already limited in brightness and contrast, exacerbate these issues during fusion, leading to severe color deviations in sescade-based two-stage models (c.g., DenseFuse, Swin- Fuse, CDDFuse). inntoorationfusiiin unified models often skew toward one task: AWFusion removes haze but alters colors, DRMF prioritizes dehazing over infrared fusion, and Text-IF favors fusion at the cost of haze removal. In contrast, TG-ECNet balances dehazing and fusion, preserving both visible details and infrared information for clearer, more natural results.
Degraded Visible Images with Defocus Blur. In the de- focus blur scenario, our method demonstrates the ability to capture and restore fine texture details effectively without losing the deblurring effect during fusion. While cascade- based two-stage models provide some degree of restoration, their performance is significantly compromised in the fu- sion stage due to the inherently low texture clarity of in- frared images. This degradation leads to a weakened deblur- ring effect in the final fusion result, as seen in SeAFusion and EMMA, where noticeable residual blurriness remains. Restoration
4.2. Restoration and Fusion Results on DeMMI-RF and EMS Dataset
To illustrate the visual differences in restored images, Fig. 5 shows fusion results of degraded visible-infrared images from our dataset after restoration and fusion. Additional results the EMS dataset provided in Appendix A.2. Compared to existing methods (e.g., DenseFuse, CDDFuse, SeAFusion,, MGDN, E.MA), our TG- ECNet better pre- serves color information during fusion. eestoorationfussionn unified models like AWFusion, TF and DRMF priori- tize fusion over degradation handling, limiting their restora- tion performance.
unified models also struggle to eliminate blur, though they outperform cascade models. However, their results still fall short compared to TG-ECNet, which maintains significantly sharper and more detailed fused output, demonstrating its effectiveness in handling defocus blur while preserving structural details.
To quantitatively evaluate the performance of different meth- ods in restoring the original image quality, we use CC, MSE, PSNR, Naty. and MS-SSIM as evaluation metrics, as shown in Table 1. The quantitative performance for each task can be found in the A.4. The quantitative results confirm that
Degraded Infrared Images with Stripe Noise. Stripe noise is specific degradation type affecting infrared images, and traditional image restoration models perform poorly in re- moving such noise. Similarly, restoration- fusion unified models tend to prioritize either restoration or fusion. For
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Task-Gated Multi-Expert Collaboration Network for Degraded Multi-Modal Image Fusion
DunseFuse
SwinPuse
CDDFuse
ScAFusion
MGDN
Degrad
EMMA
AWFusion
Text-IF
DRMF
TG-ECNes(Ous)
Figure Quantitative comparison between related works on multi-degraded scenarios.
Table 1. Comparison of average quantitative performance of single tasks across our DeMMI-RF and EMS dataset.
OuR DATASET
EMS DATASET
METHODS
MSE
PSNR
Nuks
MS-SSIN
CC
MSB
PSNR
Nosy
MS-SSIM
DENSEPUSE (Li & WU, 2018)
0,5185
0.0923
29.5794
0.0863
0.2291
0.5018
0.1090
28,9558
0.0715
0,3313
SWINFUSE (WANG AL., 2022B)
0.5279
0.0928
29,5190
0.1180
0.2418
0,5002
0.1717
28.1347
0.1176
0.1396
CDDFUSE (ZIINO AL., 2023A)
0.5286
0.0787
29.8441
0.1116
0.2359
0.5005
0.1120
28.8962
0.0933
0.3197
SEAFUSION (TANG ET AL., 2022x)
0.5288
0.0904
29.5892
0.1170
0.2265
0.5013
0.1115
28.9057
0.0874
0.3187
MGDN (GUAN ET AL., 2023)
0.5279
0.0669
30,2216
0.1195
0.2431
0,4985
0.1314
28.7083
0.0924
0.1763
EMMA (ZHАO AL.. 2024)
0,5198
0.0821
29.7471
0.1266
0.2384
0,4996
0.1132
28.8699
0.0966
0,3057
AWFUSION
AL.., 2024B)
0.5265
0.0979
29.4554
0.1429
0.2073
0.5013
0.2221
27.5428
0.2796
0.1114
TEXT-IF (Yi ET AL.. 2024)
0.0880
29.5656
0.0804
0.2379
0,5007
0.1115
28.9034
0.0800
0.3210
DRMF (TANG ET AL.. 2024)
0.5249
0.0883
29.5077
0.1203
0.2179
0.4991
0.1261
28.6382
0.0739
0.3158
TG-ECNET(OURS)
0.5340
0,0570
30.5822
0.0385
0.2972
0.5035
0.0738
29.7962
0.0405
0.4273
Table 2. Comparison of quantitative performance of multi-task and degradation free task on our DeMMI-RE dataset.
DEGRADATION TASK
METHODS
CC
MSE
PSNR
Naaf
MS-SSIM
CC
MSE
PSNR
Naaf
MS-SSIM
DENSEFUSE (Li Wu, 2018)
0.5225
0.0950
29.2285
0.1240
0.1740
0.5209
0.0819
29.7344
0.0516
0.3365
SWINFUSE (WANG ET AL., 2022B)
0.5075
0.0940
29.4485
0.1010
0.2540
0.5464
0.0659
30.2302
0.0493
0.3882
CDDFUSE (ZHAO ET AL., 2023A)
0,5225
0,0880
29.4665
0.1330
0.1790
0,5464
0.0722
30.0046
0.0653
0.3480
SEAFUSION
AL.,, 2022x)
0,5220
0.0940
29.2615
0.1240
0.1640
0,5471
0.0752
29.8970
0.0645
0.3452
MGDN (GUAN ET At.,, 2023)
0.5200
0.0790
29.6820
0.1570
0.1750
0.5475
0.0546
30.5682
0.0652
0.3676
EMMA (ZHAO ET Al., 2024)
0.5
0.0820
29.5760
0.1580
0.1600
0.5452
0.0689
30.1192
0.0682
0,3446
FWFUSION (I. AI., 20248)
0.5190
0.0990
29.1365
1.7970
0.1360
0,5458
0.0730
29.9619
0.0827
0.3244
Al., 2024)
0.5205
0.0970
0.1620
0,5462
0.0757
0.0666
0,3350
DRMF (TANG ET AL.. 2024)
0.4960
0.0900
29.3640
0.2390
0.1560
0.5449
0.0812
29.6828
0.0683
0.3259
TG-ECNET(OURS)
0.5245
0.0630
30.2200
0.0110
0.2870
0.5518
0.0455
30.9863
0.0373
0.4300
Table Ablation studies on our DeMMI-RF dataset.
NOISE AVERAGE a
15, 25,
STRIPE NOISE
SETTING
CC
MSF
PSNR
Nay
MS-SSIM
CC
MSF
PSNR
Naa
MS-SSIM
W/n TASK-AWARE GATING
0.5527
0.0430
31.136
0.028
0.411
0.5335
0.0543
30.577
0.050
0.399
W/O ALL MULTI-EXPEER BLOCKS w/o EXPERT BLOCK IN RESTORATION STAGE
0,5547
0.0399
31.314
0.030
0.396
0.5306
0.0489
30.844
0.057
0.387
0.5511
0.0410
30.014
0.044
0.386
0.5106
0.0509
29.897
0.077
0.287
w/O MULTI-EPPRT BLOCK IN FUSION STAGE
0.5523
0.0397
31.343
0.029
0.410
0.5378
0.0484
30.894
0.051
0.391
w/o TWO-STAGE TRAINING STRATEGY OURS
0.5537
0.0451
31.010
0.033
0.394
0.5313
0.0595
30.310
0.129
0.278
0.5536
0.0386
31.388
0.026
0.414
0.5310
0.0472
30.932
0.048
0.412
example, DRMF focuses on removing stripe noise but simul- tancously climinates a substantial amount useful infrared details, leading to an unnatural fusion result. Conversely, Text-IF prioritizes image fusion, resulting in incomplete noise removal. TG-ECNet achieves better trade-off, ef- fectively suppressing stripe noise while preserving the fine details of the infrared image, leading to cleaner and more informative fusion result.
types. Existing cascaded methods that first restore and then fuse images often perform poorly, leading to noise enhance- ment loss of critical image information. Unlike existing methods, TG-ECNet effectively balances these two tasks, demonstrating strong adaptability to complex degradation conditions and ensuring superior fusion quality.
4.3. Ablation Study
From these experimental settings, evident that TG- ECNet consistently outperforms other methods by maintain- ing stable restoration effect across different degradation
In this experiment, we investigate the effect of different com- ponents on task-specific restoration and fusion performance

---

Task-Gated Multi-Expert Collaboration Network for Degraded Multi-Modal Image Fusion
LITIE AOTTERTAANN
CIVV JOAT CHAAAIO
LETTE POR ICETTVENNTEN
Iiotppie sogme
DenseFuse
SwinFuse
CDDFuse
ScAFusion
MGDN
incomplete segntenmmton
FOECE seginettttion
Degrad
FMMA
AWFusion
Text-IF
DRMF
TG-BCCNe(Ours)
Figure 7, Visual results in segmentation scenario when the target is "car" with Grounded-SAM.
Table 4. Object Detection Evaluation Noise(a 50).
DENSUPUSE
SWINPUSE
CDDFUsE
SEAFUSION
MGDN
EMMA
AWPuSION
TEXT-IF
DRMF
MAP50
0,926
0.914
0.935
0,959
0.901
0.963
0.921
0.963
0.95
0.969
AP(0.5:0.95)
0.526
0.476
0.511
0.52
0.488
0.523
0.519
0.536
0.493
0.537
on performance, with a reduction in PSNR and MS-SSIM, suggesting that the multi mupei-expert block plays a critical role in guiding the model towards the most effective expert for each task. The drop in Naby further supports the importance of selection in fine-tuning restoration and fusion. Among these three settings, it can be seen that restoration expert blocks are more important for overall performance, while fusion slightly optimizes performance.
Visible
Infrared
DenseFuse
SwinFuse
CDDFuse
SeAAuuion
MGDN
EMMA
w/o Two-stage Training Strategy. Finally, we examine the effect of the two-stage training strategy by comparing with single-stage network. In this experiment, the two-stage training strategy involves first training the restoration net- work and then freezing part of its parameters while using the fusion module to generate the fused restoration results. This staged approach ensures better balance between restoration and fusion tasks. The results show that the two-stage train- ing strategy performs slightly better than the single-stage network in terms of CC, PSNR, and MS-SSIM confirm- ing that freezing certain parameters during the fusion stage helps improve the fusion quality. While the difference in Natf is minimal, the two-stage strategy proves to be more effective approach for achieving task-specific restoration and fusion.
Fusisn
Text-IF
DRMF
TG-ECNee(Ours)
Figure Visual results on real-world haze images.
using a series of ablation studies. The results are summa- rized in Table 3, which shows the performance metries for noise average (a 15, 25, 50) and stripe noise scenarios.
w/o Task-aware Gating. The first ablation study exam- ines the effect of removing the task-aware gating, which is used to guide the task-specific restoration process and multi-modal image fusion process. In this configuration, the model operates without task-aware gating, which may lead to suboptimal task-specific guidance during the fusion and restoration phases. As shown in the Table 3, the model performs slightly worse in terms of CC, MSE, PSNR, and MS-SSIM metrics, as the task prompt plays significant role in guiding the network toward optimal restoration for each specific task.
4.4. Discussion
Multiple Degradation Scenario All-in-One Image Fu- sion. this experiment, we extend our evaluation to more complex scenarios by combining multiple degradation types for restoration and fusion. In addition single-degradation tasks, we test the model's performance on mixed degrada- tion tasks, comparing our restoration-fun fuusion unified model with other methods. The experiment covers a total of 9 dif- ferent degradation combinations, which are derived from the same scenarios. We have also set other scenarios, which can be found in our DeMMI-RO dataset. However, only the most challenging settings are presented in Table 2, with the remaining combinations provided in the A.6. As shown
w/o Multi-Experi Block. We conducted three comparative experiments without expert blocks, namely removing the restoration expert block, fusion expert block, and removing both, which is responsible for expert selection and task adap- tation. By doing so, the model loses its ability to selectively choose the most appropriate expert for each task, potentially leading to performance degradation. The results show that removing the multi-en multi-expert block has a slight negative effect
8

---

Task-Gated Multi-Expert Collaboration Network for Degraded Multi-Modal Image Fusion
in Table 2, TG-ECNet outperforms all of other methods. For the Noise50 + Defocusblur + Haze + Stripe condition, TG-BCNet achieves higher PSNR, Nabf, and MS-SSIM, in- dicating superior performance in both restoration and fusion. In Fig. 6, which represents the visualization results, most methods fail to eliminate all of the degradations, resulting in inferior image quality of the fusion results. Notably, Text-IF suppressed blurring and haze, but cannot simultaneously removing noise. DenseFuse suffers from a significant loss of visible spectrum information. In contrast, TG-ECNet demonstrates superior degradation suppression while effec- tively preserving fine details, ensuring that both infrared and visible information are meaningfully integrated.
sults generated by ten models into the Grounded-SAM model (Ren et al., 2024), using the pretrained model param- eters of Grounded-SAM and selecting "car" as the prompt for the image shown in Fig. 7 to obtain the segmentation results. As shown in Fig. 7, almost all of the other methods mistakenly divide the traffic light or the electric bicycle into cars except AWFusion. Besides, SwinFuse, AwFusion, and DRMF cannot segment two cars. However, we successfully segmented the clear outlines of the two cars and did not mix them together.
5. Conclusion
We propose the Task-Gated Multi-Expert Collaboration Net- work (TG-ECNet), a novel framework for degraded multi- modal image fusion. TG-ECNet unifies image restoration and fusion into single end-to-end model, addressing chal- lenges posed by combined degradations like noise, blur, haze, and stripe noise. The core innovation is its awske gating, which integrates degradation-aware gating in the encoder and fusion-aware gating in the decoder to adapt to diverse degradation types. multi-expert collaborative framework and two-stage training strategy ensure balanced optimization. Experiments on benchmark datasets show that TG- TG-ECNet outperforms state-of-the-an art methods, improving fusion quality and robustness in challenging environments.
Degradation-Fres Scenario Image Fusion. In order to ver- ify the excellent fusion effect of the experiment on conven- tional images, we selected some data without degradation and directly compared it with all image fusion algorithms. The experimental results are shown in Table 2. Based on our experimental framework, we employ a two-stage evalu- ation process to assess comparative fusion algorithms. This methodology requires images to undergo restoration through the preprocessing network prior fusion, introducing a cas- caded processing pipeline. However, this sequential archi- tecture inevitably leads to incremental information degrada- tion, as quantitatively evidenced by the performance metrics. Restoration-fusee fusion unified models did not take this situation into consideration, obtaining a relatively weak performance. TG-ECNet has a moderate increase in all metrics.
A Acknowledgements
Real-World Degradation Scenario Image Fusion. To verify the effectiveness of the model on real-world data, we used real collected data AWMM (Li et al., 2024b) for testing, and the qualitative results are shown in Fig. 8. The results of TG-ECNet effectively suppress haze to make the image clearer, while fusing infrared information simultaneously, and the overall effect is very close to that of data provider AWFusion.
This work was supported in part the National Science and Technology Major Project under Grant 20227D01 1102200016000,,,, in part by the National Natural Science Foundation of China under Grants 62436002, 62222608, and U23B2049, in part by the Postdoctoral Fellowship Program of CPSF under Grant Number GZB20250395, in part by Tianjin Natu- ral Science Funds for Distinguished Young Scholar un- der Grant 23JCIQJC00270, in part by the Fundamental Research Funds for the Central Universities under Grant 3208002502C2, in part by Zhejiang Provincial Natural Sci- ence Foundation of China under Grant LD24F020004, and in part by the Huawei Ascend Computing Platform.
4.5. Detection and Segmentation Evaluation
Detection Evaluation. We fed the experimental results generated by ten models into the YOLOv5 model, along with degraded input images and clean images. The dataset was split into 7:3 maining-to-testing ratio, with 50 training epochs and an image resolution of 640 x 640. The detection metrics are presented in Table 4. Our method achieves state- of-the-art performance in terms of mean Average Precision (mAP) and AP(00.:0..95) 95), outperforming all compared meth- ods. This demonstrates the effectiveness of our framework in accurately detecting and localizing objects under various degradation conditions. The superior performance in these metrics highlights the robustness and generalization ability of our approach.
Impact Statement
This paper propose a unified framework for degraded multi- modal image restoration and fusion, which bridges differ- ent tasks together through two-stage training strategy to learn inter-task information while avoiding mutual interfer- ence, enabling all-in-one processing. This work construct a large-scale degraded multi-modal image fusion benchmark, DeMMI-RF, which contains more than 30, 000 multi-modal data of different degradation types, including those from UAVs and driving viewpoints. This dataset will be of greater significance to the field.
Segmentation Evaluation. We fed the experimental re-

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Task-Gated Multi-Expert Collaboration Network for Degraded Multi-Modal Image Fusion
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Task-Gated Multi-Expert Collaboration Network for Degraded Multi-Modal Image Fusion
A. Appendix.
Table S. Mixture of experts hyperparameter selection.
A.1. The haze Setting
ALL TASK AVERAGi
SETTING
CC
MSE
PSNR
Nabs
MS-SSIM
The traditional method for adding haze to an image is the atmospheric scattering model, with the formula:
2 IN 3
0,5531
0,0408
31,2374
0.0258
0,4087
IN
0.5529
0.0411
31.2207
0.0269
0.4056
IN 7
0.5525
0.0446
31.0421
0.0233
0.4053
(x) 1 ((x) ((x)
IN 9
0.5520
0.0409
31.2360
0.0247
0.4069
((x) - A +b
(7)
6 IN 11
0,5512
0.0397
31.3086
0.0223
0.4206
((:)
7 13
0.5498
0.0408
31.2459
0.0235
0,4113
3 IN 11
0,5512
0.0420
31.1842
0.0270
0.4074
Where ((x) indicates image with haze, (1) indicates image without haze, A indicates atmospheric light intensity. In our work, set A as:
9 IN 11
0,5519
0.0411
31,2394
0,0226
0,4094
11 IN 11
0.5523
0.0410
31.2273
0.0232
0,4115
Table 6. Mixture of experts block setting.
À (E))(J(E))
8
SETTING
AL.. TAsK AVERAGR
CC
MSE
PSNR
Nag
MS-SSIM
W/O MoE ()EX!)
0,5519
0.0403
31.2981
0.0224
0.4180
which avoids the image being too bright and makes it close to reality.
w/o MOE-I&((EX2)
0,5517
0.0440
31,0731
0.0302
0.4037
W/O 2
0,5525
0.0397
31.2858
0.0232
0.4165
w/o ((E
0,5530
0.0423
31.1435
0.0264
0.4123
w/o M0E-2(EX133
0.5516
0.0403
31.2642
0.0217
0.4143
A.2. Qualitative performance on each task in EMS dataset.
w/o MoE-3(Ex12 12)
0,5531
0.0414
31.2007
0,0279
0.4137
wio PUsIONMoR
0,5514
0.0425
31,1531
0.0271
0.4144
ALL EXPERT
0,5537
0.0397
31,3086
0.0216
0.4206
As shown in Fig. 9, qualitative comparison has been made on each task in EMS dataset. Since the EMS dataset utilized some lower clarity datasets, the overall effect appears blurry.
A.3. Mixture of Experts block Setting
In our experiments, we explored the optimal configuration for the Mixture of Experts (MoE) system in subset of the DeMMI-RF dataset by testing various expert selection modes, including no experts, 3 experts selecting 2, 5 experts selecting 3, 7 experts selecting 4, 9 experts selecting 5, 11 experts selecting 6, and 13 experts selecting 7. The results, as shown in Table 5, indicate that the 11 experts selecting configuration achieves the best performance, highlighting the importance of balancing task specialization and computational efficiency. Additionally, we compared different expert quantities within the 11-expert system (11 selecting 3, 11 selecting 6, and 11 selecting 9) and found that increasing the number of selected experts does not always improve restoration or fusion performance, further confirming the need for an optimal expert selection strategy.
Degraded Visible Images with Noise. In the denoising task, our method effectively suppresses noise and makes the edge information of the image clearer. Except for Text-IF, most methods cannot effectively suppress noise. Although Text-IF can better preserve color information, it does not preserve contour information very well. This is enough to demonstrate the effectiveness of our method.
Degraded Visible Images with Haze. The performance of different methods in dehazing tasks varies. DenseFuse, CDDFuse, SwinFuse, EMMA, and DRMF cannot com- pletely remove haze. SwinFuse, MGDN, and AWFusion have caused significant color distortion. Although Text-IF removed most of the haze, it did not completely preserve the profile of people and car, and our method outperformed it in this regard.
We integrated Multi-Expert (MoE) collaborative modules before the transformer blocks of the first three encoders and after the last decoder's transformer block. Ablation studies confirm their effectiveness: in the restoration module, a three-level MoF system outperformed partial configurations (one or two levels), with full retention yielding optimal feature extraction (Table 6). Similarly, removing the MoE from the fusion module severely degraded performance, highlighting its necessity for feature aggregation and high- quality fusion.
Degraded Visible Images with Defocusblur. MGDN and DRMF cannot fully restore clear images in defocusbluring tasks. The remaining methods have achieved the effect of deblurring, but the image information is too dependent on infrared images, resulting in the loss of color details.
Degraded Infrared Images with Stripe Noise. the stripe task, our method not only effectively suppresses stripe noise, but also achieves a balance between color information and thermal information.
Overall, these experiments validate the effectiveness of our MoE-based design, particularly the 11 experts selecting 6 configuration and the strategic placement of MoE systems across the restoration and fusion modules. These findings
In summary, our method still achieved excellent results on the EMS dataset, which proves the robustness of our method.
12

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Task-Gated Multi-Expert Collaboration Network for Degraded Multi-Modal Image Fusion
DeneFoos
Fair
SaNTube
MUUN
IKMA
DISA
Tom woR when valtte rage autluing fves liee
Teyut
IniiFar
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INMA
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Seatas
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Figure 9. Qualitative comparisons of various methods on EMS dataset.
Camve segreeention
Incomplee less segrtmmttionn
Lerooe sepmenattion
Cenur scamertation
DenseFuse
SwinFuse
CDDFuse
ScAFusion
MGDN
HETT sermmntttion
Degrad
EMMA
AWFusion
Text-IF
DRMF
TG-ECNe((Ous))
Figure 10. Visual results in segmentation scenario when the target is person with Grounded-SAM,
not only optimize the performance of our framework but also provide valuable insights for future research on task- specific expert systems in multi-modal image processing.
achieved multiple optimal metrics on two datasets in the deblurring task. Due to MGDN's ability to achieve multi focus image fusion, it has achieved suboptimal level.
Degraded infrared Images with Stripe Noise in DeMMI- RF and EMS dataset. As shown in Table 11 our method achieved the best performance all metrics in the task of removing stripe noise on two datasets.
A.4. Quantitative performance on each task
Degraded Visible Images with Noise. As shown in Table and Table 8, we achieved the best results multiple metrics for both settings. DenseFuse relies more on infrared images, which, to some extent, suppresses Gaussian noise in visible light images, while Text IF has better noise recognition ability and achieves relatively good results. Other methods, such as CDDFuse, MGDN, and AWFusion only perform well on one or two metrics.
Our experimental results demonstrate that the proposed method achieves superior and robust performance across both benchmark datasets. Quantitatively, the approach shows significant improvements key metrics, outperform- ing current state-of-the-ar methods. The method's strength lies in its ability to maintain an optimal balance between noise suppression and detail preservation.
Degraded Visible Images with Haze DeMMI-RF and EMS dataset. As shown in Table 9, the dehazing task, due the different methods of generating haze, the perfor- mance between the two datasets is different. Our method achieved the best results in multiple metrics both datasets. On the EMS dataset, DRMF achieved a suboptimal level, while AWFusion, which specializes in image dehazing, achieved the best performance on CC.
A.5. Other Segmentation Results
We also fed the experimental results, with three persons riding bicycle or an electric bicycle, generated by ten models into the Grounded-SAM model (Ren et al., 2024), using the pretrained model parameters of Grounded-SAM and selecting "person" as the prompt for the image shown in Fig. 10 obtain the segmentation results. As shown in Fig. 10, CDDFuse, Fusion,,, and MGDN mistook the
Degraded Visible Images with Defocusblur in DeMMI- RF and EMS dataset. As shown in Table 10, method
13

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Task-Gated Multi-Expert Collaboration Network for Degraded Multi-Modal Image Fusion
Table
Quantitative comparison on our DeMMI-RF dataset in different Noise settings.
DENOISE
METHODS
OUR DATASET (N0
OUR DALASET (NOSISE.-25)
MSE
PSNR
Naks
MS-SSIM
CC
MSE
PSNR
Nows
MS-SSIM
DENSEPUSE (LI & WU, 2018)
0.5200
0.1028
29.2159
0.0850
0.2139
0.5198
0.0927
29.4729
0,0805
0.2228
SWINFUSE (WANG ET AL., 2022B)
0.5272
0.0977
29.2750
0.1453
0.1930
0,5372
0.0936
29.3787
0.1377
0.2116
CDDFUSE (ZHAO AL., 2023A)
0.5266
0,0896
29.4820
0.1265
0.2135
0.5366
0.0798
29.7036
0.1201
0.2194
SEAFUSION (TANG ET AL.,, 2022A)
0.5269
0.1062
29.1165
0.1303
0.2075
0,5362
0.0926
29.4142
0.1209
0.2170
MGDN (GUAN ET AL., 2023)
0,5262
0,0746
29.8921
0.1299
0.2286
0,5350
0.0656
30.1583
0. 0.1268
0,2291
EMMA (ZHAO ET AL., 2024)
0,5190
0.0902
29.4201
0.1758
0.1961
0,5280
0.0795
29.7504
0.1728
0.1917
AWFUSION (LI ET AL.,, 2024B)
0.5244
0.1108
29.0827
0.1501
0.1874
0.5340
0.0890
29.5590
0.1399
0.1961
TEXT-IF (Yi AL.,, 2024)
0.5253
0.0957
29.3726
0.0773
0.2314
0.5345
0.0858
29.6254
0.0740
0.2385
DRMF (TANG ET AL.,, 2024)
0.5211
0.1062
29.1004
0.1702
0.1803
0.5308
0.0835
29.6678
0.1675
0.1882
TG- (CUET(OURS)
0.5274
0.0602
30.3239
0.0395
0.2877
0,5382
0.0570
30.4800
0.0378
0.2858
Table 8. Quantitative comparison on our DeMMI-RF and EMS dataset Noise Setting.
DENOISE
METHODS
OuR DATASET (NoSISEr=1))
EMS DATASET
Naay
MS-SSIM
PSNR
Naay
MS-SSIM
DENSEFUSE (LI & Wu, 2018)
0,5133
0.0890
29.5150
0.1026
0.2099
0.4760
0.1040
29.0510
0.1080
0.1720
SWINFUSE (WANG
2022b)
0.5232
0.0922
29.3806
0.2026
0.1847
0.4755
0.1360
28 28.4600
0.1970
0.0720
CDDFUSE (ZHAO AL., 2023x)
0.5245
0.0785
29.7360
0.1751
0.1968
0.4730
0.1150
28.8275
0.1820
0.1540
SEAFUSION (TANG ET AL.,, 2022A)
0.5257
0.0846
29.5466
0.1833
0.1840
0.4745
0.1060
28.9915
0.1330
0.1620
MGDN (GLAN ET AL., 2023)
0,5225
0.0672
30.0439
0.2010
0.1885
0.4755
0.0887
29.4320
0.1450
0.0950
EMMA (ZHAO
2024)
0,5152
0.0794
29.6692
0.2538
0.1518
0.4735
0.1140
28.8270
0.1690
0.1520
AWFUSION (LI AL., 2024B)
0.5241
0.0810
29.6963
0.1843
0.1735
0,4890
0.1630
28.0385
0,3980
0,0450
TEXT-IF (YI ET AL., 2024)
0.5251
0.0785
29.7154
0.1263
0.1934
0.4765
0.1070
28.
0.0930
DRMF (TANG ET AL., 2024)
0,5162
0,0880
29.4381
0.2567
0.1275
0,4695
0.1330
28.4930
0.1330
0,1680
TG-ECNET(OURS)
0.5281
0.0546
30.5374
0.0374
0.2678
0.4735
0.0880
29.4120
0.0470
0.2300
ground person. Besides, almost all of the methods segmented bicycles as part of person and could not separate two people. However, we successfully segmented the clear outlines of the three people and did not mix them together.
A.8. Computing Efficiency Verifications
As shown in the Table 17, we compared the computational efficiency of difffrrnnt methods and different MoE settings, which can demonstrate the effectiveness of our setting.
A.6. Multiple Degradation Results
As shown in Table 12. 13, 14, and 15, TG-ECNet outper- forms both DRMF and Text-IF across diverse degradation conditions shown. These results further demonstrate that TG-BCNet consistently outperforms existing methods, even under more complex degradation scenarios.
A.7. Weight Setting Verifications
In the experiment, after obtaining the restored visible and infrared features, we use trainable weights to obtain fused features. To verify the effectiveness of this experimental set- ting, we conducted the following comparative experiments: a). visible modality dominant(Vil VIS Major), inffrrrd modal- ity dominant(IR Major), c).direct VIS-IR modality addi- tion(plus). Among them, major means to obtain fused fea- tures with ratio of 90 percent and another modality of 10 percent. As shown in Table 16, our setting can achieve better performance.
14

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Task-Gated Multi-Expert Collaboration Network for Degraded Multi-Modal Image Fusion
Table 9. Quantitative comparison our DeMMI-RF and EMS dataset in Haze Setting.
DEHAZING
METHODS
OUR DATASET
EMS DATASET
CC
MSE
PSNR
Nat
MS-SSIM
CC
MSB
PSNR
Nob
MS-SSIM
DENSEFUSE WU, 2018)
0.5205
0.0977
29.4179
0.0705
0.2418
0.4795
0.0900
29.3665
0.0603
0.1998
SWINFUSE (WANG ET AL., 2022b)
0.5324
0.1067
29.0076
0.0523
0.3128
0.4835
0.1430
28.3600
0.0950
0.0950
CDDFUSE (ZHAO ET AL.. 2023A)
0.5361
0.0761
29.8585
0.0607
0.2829
0.4785
0.1000
29.1615
0.0797
0.2111
SEAFUSION (TANG ET AL.. 2022A)
0.5367
0.0917
29.5512
0.0788
0.2574
0.4790
0.0960
29.2265
0.0831
0.1978
MGDN (GLAN AL.,, 2023)
0.5372
0.0706
30.0563
0.0638
0.2761
0.4810
0.0840
29.5095
0.0680
0.1210
EMMA (ZHAO ET AL.., 2024)
0.5187
0.0933
29.3895
0.0476
0.3383
0.4775
0.1030
29.0635
0.0877
0,2000
AWFUSION (LI ET AL., 2024B)
0,5342
0.1101
29.1808
0.1043
0.2394
0.4930
0.1720
27.9195
0.3630
0.0550
TEXT-IF (Yi ET AL.. 2024)
0.5375
0.0947
29.4805
0.0732
0.2561
0.4785
0.1030
29.0580
0.0855
0.1927
DRMF (TANG ET AL.,, 2024)
0.5239
0.0796
29.7185
0.0571
0.2969
0.4865
0.0940
29.2695
0.0253
0.2725
TG- ECNET(OURU)
0.5394
0.0621
30.3676
0.0419
0.3204
0.4855
0.0720
29.8560
0.0242
0.2732
Table 10. Quantitative comparison DcMMI-RE and EMS dataset in Blur Setting.
DEBLUR
METHODS
OUR DATASET
EMS DATASET
CC
MSR
PSNR
Nat
MS-SSIM
CC
MSB
PSNR
Noay
MS-SSIM
DENSEFUSE WU, 2018)
0.5169
0.0851
30.1249
0.0507
0.2772
0,4750
0.1040
29.0505
0,0650
0.1920
SWINFUSE (WANG ET AL., 2022B)
0.5236
0.0804
30.3363
0.0436
0,3191
0,4745
0.1320
28.5435
0.1040
0.0860
CDDFUSE (ZHAO ET AL.. 2023A)
0,5253
0.0745
30.2695
0.0557
0.2880
0.4720
0.1170
28,8010
0,0850
0.1820
SEAFUSION (TANG AL., 2022
0.5250
0.0842
30.0952
0.0598
0.2843
0.4735
0.1100
28.9290
0.0850
0.1800
MGDN (GUAN ET AL., 2023)
0.5251
0.0599
30,8097
0.0541
0,3119
0.4755
0.0873
29.2546
0.0740
0.1130
EMMA (ZHAO ET AL.. 2024)
0,5214
0.0742
30.2619
0.0470
0.2858
0.4720
0.1190
28.7475
0.0850
0.1860
AWFUSION (LI ET AL.,, 2024B)
0.5226
0.0886
29.9665
0.0847
0.2753
0.4860
0.1550
28.1520
0.3350
0.0590
TEXT-IF (Yi AL.. 2024)
0.5371
0.0915
29.4744
0.0565
0.2686
0.4760
0.1060
29.0125
0.0900
0.1860
DRMF (TANG BT AL.. 2024)
0.5342
0.0968
29.2727
0.0338
0.2576
0.4680
0.1310
28.5305
0.0630
0.1940
TG- (G-ECNET(OURS)
0.5409
0.0525
31.0968
0.0274
0.3275
0.4775
0.0850
29.4790
0.0360
0.2500
Table 11. Quantitative comparison on our DeMMI-RF and EMS dataset Stripe Setting.
DESTRIPE
METHODS
OUR DATASET
EMS DATASET
MSE
PSNR
Nayy
MS-SSIM
CC
MSB
PSNR
Nosy
MS-SSIM
DENSEPUSE (LI & WU, 2018)
0,5210
0.0893
29.4865
0.1503
0.1829
0.5200
0.1160
28.8095
0.0670
0,4360
SWINFUSE (WANG ET AL., 2022B)
0,5250
0.0904
29.4007
0.1710
0.1826
0,5165
0.1970
27.9010
0.1070
0.1800
CDDFUSE (ZIIAO AL., 2023A)
0.5231
0.0759
29.8123
0.1661
0.1824
0,5195
0.1130
28.8715
0.0770
0.4200
SEAFUSION (TANG ET AL., 2022A)
0.5226
0.0857
29.5779
0.1624
0.1757
0,5200
0.1170
28. 8018
0.0780
0,4200
MGDN (GUAN ET AL., 2023)
0.5216
0.0663
30.1047
0.1812
0.1852
0,5140
0.1640
28.2040
0.0900
0,2250
EMMA (ZHIOO ET AL.. 2024)
0,5154
0,0780
29.7903
0.1068
0.2336
0,5180
0.1140
28.8630
0.0840
0,3980
AWFUSION
AL.. 2024B)
0,5204
0.1118
29.0121
0.2304
0.1329
0.5100
0.2650
27.1820
0.2170
0.1540
TEXT-IF (Yi ET AL.. 2024)
0.5216
0.0790
29.7895
0.0876
0.2224
0,5180
0.1160
28.8195
0.0730
0.4180
DRMF (TANG ET AL.. 2024)
0.5183
0.0717
29.9618
0.0814
0.2305
0,5170
0.1310
28.5460
0.0740
0.3920
TG-ECNET(OUR)
0.5259
0.0572
30.4625
0.0525
0.2762
0.5215
0.0680
29.9525
0.0440
0.5560
Table 12. Multiple degradation all-in-one results in Appendix A.6.
DEFOCUS+HAZE
NOISE5O+HAZZI-STUUPE
METHODS
CC
MSF
PSNR
Noay
MS-SSIM
CC
MSE
PSNR
Naky
MS-SSIM
DRMF (TANG ET AL.. 2024)
0.5025
0,0770
29.851
0.008
0.310
0.4945
0.0720
29.788
0.277
0.171
TEXT-IF (YLET AL., 2024)
0.5250
0.0930
29.272
0.026
0.278
0.5620
0.0900
29.295
0.170
0.160
OURS
0.5225
0.0610
30.298
0.017
0.315
0.5650
0.0460
30.733
0.022
0.273
Table Multiple degradation all-in one results Appendix A.6.
5
METHODS
CC
MSE
PSNR
Noaf
MS-SSIM
CC
MSE
PSNR
Navs
MS-SSIM
DRMF (TANG ET AL., 2024)
0.5260
0.1490
28.202
0.086
0.244
0.4335
0.0840
29.600
0.197
0.276
TEXT-IF (Yi ET AL., 2024)
0.5240
1050
28.974
0.025
0.327
0.3525
0.0930
29.239
0.038
0.383
OURS
0.5190
0.0780
29.631
0.010
0.380
0.3435
0.0630
30.
0.015
0.423
15

---

Task-Gated Multi-Expert Collaboration Network for Degraded Multi-Modal Image Fusion
Table 14. Multiple degradation all-in-one results in Appendix A.6.
NOISE25+DEFOCUS
NOISE25+HAZE
METHODS
CC
MSE
PSNR
Naaf
MS-SSIM
CC
MSE
PSNR
Nabs
MS-SSIM
DRMF (TANG ET AL., 2024)
0.5255
0.1470
28.232
0.150
0.208
0.4915
0.0930
29.427
0.128
0.212
TEXT-IF (YIE) ET AI.,, 2024)
0.5285
0.1040
28.989
0.070
0.273
0.4985
0.0960
29.205
0.068
0.231
OURS
0.5265
0.0720
29.776
0.012
0.370
0.4990
0.0710
29.944
0.017
0.299
Table Multiple degradation all one results Appendix A.6.
DESSESO
HOISE50+HAZZ
METHODS
CC
PSNR
Noks
MS-SSIM
CC
MSE
PSNR
Nabs
MS-SSIM
DRMF (TANG 2024)
0.5035
0.1500
28.197
0.239
0.180
0.4945
0,0700
29.847
0.239
0.178
TEXT-IF EYLET AL., 2024)
0.5170
0.
28.822
0.177
0.201
0.5715
0.0800
29.561
0. 009
0.189
OURS
0.5200
0.0740
29.730
0.018
0.371
0.5715
0.0500
30.559
0.035
0.267
Table 16. Weight setting verifications.
ALL TASK AVERAGE
SETTING
CC
MSE
PSNR
Nabs
MS-SSIM
VIS MAJOR
0.5430
0.0488
29.8896
0.0533
0.4184
IR MAJOR
0.5419
0.0499
29.6783
0.0483
0.4119
PLUS
0.5548
0.0482
29.9257
0.0476
0.4198
LEARNABLE WEIGHTS
0.5537
0.0397
31.3086
0.0216
0.4206
Table 17. Computing efficiency verifications.
METHODS
FPS
TOTAL PARAMS
MOE SETTINGS
FPS
TOTAL PARAMS
DENSEFUSE (LI WU, 2018)
1.64
116.00IM
1.30
146.135M
SWINFUSE (WANG ET AL., 2022B)
1.20
239.029M,
21N3
1.20
150.967M
CDDFUSE (ZHAD AL., 2023A)
1.03
119.730M
31NS
1.16
153.441M
SEAFUSION (TANG ET AL.. 2022A)
164.402M
1.12
155.915M
MGDN (GUAN ET AL., 2023)
1.39
117.910M
SIN9
1.08
158.389M
EMMA (ZHAO
2024)
1.49
121.029M
1I
1.03
160.863M
AWFUSION (LI AL.,, 2024B)
192,900M
167.337M
TEXT-IF (Yi ET AL., 2024)
3.09
580. 850M
3IN11
1.00
160.863M
DRMF (TANG ET AL., 2024)
0.20
2610.205M
91N
0.92
160.863M
TG-ECNETTOURS)
160.863M
160.863M
16


Reviews:
[{"review_id": "zzPoRww6FY", "reviewer": "Reviewer_oppE", "summary": "This paper introduces TG-ECNe, a novel framework designed to address the challenges of degraded multimodal image fusion. Multimodal images, such as visible and infrared images, often suffer from degradations like noise, blur, haze, and stripe noise, which negatively impact fusion quality. TG-ECNet tackles these issues by incorporating a task-gated router that includes degradation-aware gating in the encoder and fusion-aware gating in the decoder.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for recognizing our contributions in this work. We will reply to your questions in order.\n\n---\n\n## Response to visualization results in combined degradation scenarios\n#### In Fig.1, we’ve shown the performance of some methods like Text-if and DRMF in combined degradation scenarios. We use AdaIR(ICLR2025) as pre-processing before fusion algorithms to ensure the fairness of the experiments and show the visualization results and quantitative comparisons in [Newfig1](https://anonymous.4open.science/r/TG-ECNet/NewFig1.jpg), where our method outperforms all other methods.\n--- \n\n## Response to the proposed dataset\n#### In our DeMMI-RF dataset, the haze scenarios are generated by the atmospheric scattering model(ASM), where we change the parameter $A$ to adjust the intensity close to real situations. From the traditional ASM, we know:\n\n#### $$J(x)=\\frac{1}{t(x)}I(x)-A\\frac{1}{t(x)}+b$$\n\n#### Where $t(x)$ indicates image with haze, $J(x)$ indicates image without haze, $A$ indicates atmospheric light intensity. In our work, we set $A$ as $\\overline{A}=MEAN(J(x))$, which avoids the image being too bright and makes it close to reality.\n#### The noise scenarios are aligned with other Restoration Model settings. \n#### Besides, we utilize real-world data from the AWMM dataset[r1] to validate the robustness of our method in [Newfig2](https://anonymous.4open.science/r/TG-ECNet/NewFig2.png). In [Newfig2](https://anonymous.4open.science/r/TG-ECNet/NewFig2.png), we show the visualization comparison among related works and ours. The dataset consists of images with snow and haze. In our experiment, the results of our method have improved clarity and contrast.\n---\n\n## Response to the methods designed for adverse weather conditions\n#### Comparisons with AWFusion [r1] have been made in [NewFig1](https://anonymous.4open.science/r/TG-ECNet/NewFig1.jpg), [Newfig2](https://anonymous.4open.science/r/TG-ECNet/NewFig2.png), [NewFig3](https://anonymous.4open.science/r/TG-ECNet/NewFig3.png), [NewFig4](https://anonymous.4open.science/r/TG-ECNet/NewFig4.png), [NewFig5](https://anonymous.4open.science/r/TG-ECNet/NewFig5.png) and [Newfig6](https://anonymous.4open.science/r/TG-ECNet/NewFig6.png). Our method demonstrates superior performance across various scenarios. We will duly incorporate a citation to this work in the revised manuscript.\n---\n\n## Response to the computational efficiency\n#### Although our multi-expert network inevitably increases the computational complexity, our method has a smaller parameters than some of the other methods. As for the number of experts, we show the quantitative comparisons between related works and other MoE settings in our work on the performance and efficiency in [NewFig4](https://anonymous.4open.science/r/TG-ECNet/NewFig4.png), which shows our choice is best when considering the efficiency and performance. Compared with other methods, our method has a medium cost but shows a good performance. In the selection of the number of experts, our selection shows the best performance.\n---\n\n## Response to innovations\n#### The traditional two-stage approach (image restoration followed by fusion) may be contradictory. Image restoration tends to eliminate noisy information while image fusion tends to integrate more valid information, but the restoration model may eliminate information of interest for image fusion in the first stage, leading to sub-optimal fusion performance. In our work, the proposed unified framework realizes the divide-and-conquer treatment of different degradation tasks by arranging the corresponding experts to process dynamically through Degradation-Aware Gating performing task routing, while the multi-expert system guided by Fusion-Aware Gating dynamically equilibrates the degree of information retention between the fusion and restoration tasks to realize the better restoration and fusion results. Our framework reduces the contradiction between the restoration and fusion tasks and minimizes the information loss in the cascaded structure. Also, the structure doesn’t need text guidance, which is a must for Text-if and is more adaptive for multitasks than DRMF. Such a network can recognize different degradations and extract effective features.\n---\n\n## Response to experiments in non-degraded scenarios\n#### We’ve shown the results in non-degraded scenarios in [NewFig5](https://anonymous.4open.science/r/TG-ECNet/NewFig5.png). From the results, we can know that our method is immune to the loss of features between cascaded networks.\n--- \nThanks for your suggestions.\n\n[r1] Li, et al. AWFusion. arXiv:2402.02090,2024.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "Yes", "experimental_designs_or_analyses": "Yes", "relation_to_broader_scientific_literature": "No", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "1. The experiments lack visualization results in combined degradation scenarios. Furthermore, since existing All-in-One image fusion algorithms are incapable of handling composite degradation, it is necessary to compare them with image restoration algorithms used as pre-processing steps to ensure the fairness of the experiments.\n\n2. The article lacks detailed information about the proposed dataset, DeMMI-RF. The haze and noise scenarios do not align with real-world degradation conditions. Firstly, infrared images often suffer from varying degrees of degradation in adverse weather conditions, which the DeMMI-RF dataset does not seem to account for. Secondly, infrared images are more susceptible to noise than visible light images, yet the paper does not include experiments where both modalities are affected by noise.\n\n3. In haze scenarios, comparisons should be made with current image fusion algorithms designed for adverse weather conditions, such as AWFusion.\n\n4. The authors employ a multi-expert network to handle different types of degradation. However, could this lead to high computational complexity for the proposed algorithm? An analysis of computational efficiency is lacking.\n\n5. The proposed framework does not appear to introduce any theoretical innovations at the module level. A more in-depth analysis is needed to explain why the proposed framework can effectively address challenges in complex scenarios.\n\n6. Experiments in non-degraded scenarios are lacking, which are necessary to validate the generalization capability of the proposed algorithm.", "other_comments_or_suggestions": "No", "questions_for_authors": "See the Weaknesses", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ozaf1DnsF5", "reviewer": "Reviewer_LrEX", "summary": "This paper presents TG-ECNet, a unified framework that concurrently addresses restoration and fusion of degraded visible images (affected by noise, blur, and haze) and infrared images (with stripe noise) through a task-gated router and multi-expert collaboration mechanism. The proposed integration of restoration and fusion processes enhances model robustness, demonstrating improved performance in real-world scenarios. Furthermore, the authors introduce a new benchmark dataset containing degraded multi-modal images.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for recognizing our contributions in this work. We will reply to your questions in order.\n\n---\n\n## Response to the selection of experts\n#### In our work, we select the top 6 experts from 11 experts to cope with different degradations. In our paper, there is a typo in Line 218. We utilize degradations of three types of Gaussian noise, haze, defocus blur, and stripe noise. The number of types is aligned with the number of experts we select. In [NewFig4](https://anonymous.4open.science/r/TG-ECNet/NewFig4.png), we compare the efficiency and performance of different methods and different expert selections in our method, which demonstrates that our selection is reasonable.\n--- \n\n## Response to the impact of MoE configurations\n#### In [NewFig4](https://anonymous.4open.science/r/TG-ECNet/NewFig4.png), we have shown that our method has a medium standard of FPS and Params and achieves a great performance. As the number of experts increases, the tasks each expert needs to complete are reduced, allowing them to focus on features specific to a particular degradation. Although the computational difficulty is somewhat heightened, it increases the granularity of the experiments.\n\n---\n## Response to the datasets\n#### Our dataset includes six types of degradation: Noise15, Noise25, Noise50, Haze, DefocusBlur, and StripeNoise, with quantities of 726, 723, 724, 909, 3094 and 2443 respectively. Since we consider the DefocusBlur task to be more challenging and the infrared modality only involves one type of degradation StripeNoise, these two tasks have a larger volume of data. Additionally, we have created multi-degraded scenarios where a single image pair may contain 2 to 6 types of degradation, with 25 images allocated for each configuration.\n--- \n\n## Response to the setting of the object detection\n#### For each test set containing different degradations, we split the images in an 8:2 ratio and input them into the YOLOv5 network. The training was configured with 50 epochs, an image size of [640, 640], and we calculated both AP and mAP@0.5:0.95. The entire training process was completed on a single RTX 3090 GPU.\n--- \n\n## Response to essential references\n#### We conducted a series of experiments using MGDN [r2], with the corresponding results presented in [NewFig1](https://anonymous.4open.science/r/TG-ECNet/NewFig1.jpg), [Newfig2](https://anonymous.4open.science/r/TG-ECNet/NewFig2.png), [NewFig3](https://anonymous.4open.science/r/TG-ECNet/NewFig3.png), [NewFig4](https://anonymous.4open.science/r/TG-ECNet/NewFig4.png), [NewFig5](https://anonymous.4open.science/r/TG-ECNet/NewFig5.png) and [Newfig6](https://anonymous.4open.science/r/TG-ECNet/NewFig6.png). Our method outperformed MGDN [r2] across all tasks. We will incorporate a citation to this work in the revised manuscript.\n---\n\n## Response to ablation studies on critical components\n#### In [NewFig7](https://anonymous.4open.science/r/TG-ECNet/NewFig7.png), we have conducted two other ablation experiments. First, since we use a learnable mask in the fusion stage, we compare this setting with visible modality dominant(VIS Major), infrared modality dominant(IR Major), and direct VIS-IR modality addition(plus). The result demonstrates that our setting is suitable for different scenarios. Second, since we use the multi-expert collaboration module in both stages, we conduct experiments in which only one stage uses the multi-expert collaboration module. In the experiments, we demonstrate that the module is instrumental in both stages.\n---\n\n## Response to the analysis of computational efficiency\n#### We have responded to it above. Related results can be seen in [NewFig4](https://anonymous.4open.science/r/TG-ECNet/NewFig4.png).\n---\n\n## Response to other comments or suggestions\n#### 1. Lines 211-213 and 213-215 don’t contain repetitive content. Lines 211-213 denote the setting of the first stage of the network, while lines 213-215 denote the setting of the second stage of the network.\n#### 2. Thanks for this suggestion. We have adjusted the order in time order both in the table and in the visualization.\n#### 3. Thanks for correcting this typo.\n--- \nThanks for your suggestions.\n\n[r2] Guan, et al. Mutual-guided dynamic network for image fusion. ACM MM, 2023.", "claims_and_evidence": "Insufficient substantiation:\n1. The use of only two experts in the multi-expert collaboration module raises questions about the efficacy of this core design compared to conventional multi-expert architectures.\n2. No analysis is provided regarding the impact of MoE configurations on model parameters and computational overhead.", "methods_and_evaluation_criteria": "Not sufficiently justified:\n1. Inadequate specification of degradation parameters and data characteristics for both proposed benchmark datasets.\n2. Experimental details on the detection task implementation remain unclear.", "theoretical_claims": "The paper primarily makes the following theoretical claims:\n1. Multi-modal image fusion must account for potential degradations in both RGB and infrared modalities\n2. Existing multi-modal fusion methods underperform when processing degraded input images\n3. The task-gated router enables adaptive handling of degraded features during fusion\n4. The multi-expert collaborative network achieves robust restoration and high-quality fusion simultaneously\n5. A two-stage training strategy optimizes joint handling of restoration and fusion tasks", "experimental_designs_or_analyses": "Please refer to previous comments.", "relation_to_broader_scientific_literature": "This work connects to:\nAll-in-one image restoration approaches.\nMulti-modal image fusion methodologies.\nMoE (Mixture of Experts) techniques.\nDynamic network architectures.", "essential_references_not_discussed": "While the core methodology relates to dynamic networks for image fusion, the authors omit discussion of Mutual-guided Dynamic Network for Image Fusion (ACMMM 2023), a directly relevant contemporaneous work.", "other_strengths_and_weaknesses": "Strengths:\n- Introduction of novel benchmark dataset for degraded multi-modal restoration/fusion\n- Comprehensive experiments demonstrating SOTA performance on proposed and existing datasets\n- Persuasive visual comparisons showing qualitative improvements\nWeaknesses:\n- Limited ablation studies on critical components\n- Insufficient analysis of computational efficiency", "other_comments_or_suggestions": "- Lines 211-213 and 213-215 contain repetitive content\n- Discrepancy exists between table organization (recent methods first) and visual/metric presentation order\n- Typographical error in Line 531 (\"table()\") requires correction", "questions_for_authors": "Please refer to previous comments.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "jYxX2zxOen", "reviewer": "Reviewer_d8Ku", "summary": "The paper introduces Task-Gated Multi-Expert Collaboration Network (TG-ECNet), a novel framework designed to address the challenges of degraded multimodal image fusion. The key innovation lies in its task-gated router, which integrates degradation-aware gating in the encoder and fusion-aware gating in the decoder to dynamically adapt to various degradation types (e.g., noise, blur, haze, stripe noise) and selectively aggregate features from multiple modalities (e.g., visible and infrared images). The framework also employs a multi-expert collaborative network and a two-stage training strategy to balance restoration and fusion tasks, ensuring high-quality fusion results in complex real-world scenarios. The authors demonstrate the effectiveness of TG-ECNet through extensive experiments on both synthetic and real-world datasets, showing superior performance compared to state-of-the-art methods in terms of restoration and fusion quality.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for recognizing our contributions in this work. We will reply to your questions in order.\n\n---\n\n## Response to the real-world experiments\n#### We utilize real-world data from AWMM dataset to validate the robustness of our method in [Newfig2](https://anonymous.4open.science/r/TG-ECNet/NewFig2.png). The dataset consists of images with snow and haze. In our experiment, the results of our method have improved clarity and contrast.\n--- \n\n## Response to the downstream tasks\n#### In [NewFig6](https://anonymous.4open.science/r/TG-ECNet/NewFig6.png), we employed the unified segmentation network GroundingSAM to evaluate segmentation performance.\n#### For the noisy scenario with $\\sigma$=50, we segmented cars in the image, demonstrating that our method produces no false detections.\n#### For infrared images with stripe noise, we segmented humans in the image, showing that our method accurately extracts the contours of all three individuals without missed detections or misclassifying the e-bike as part of the targets. \n---\n\n## Response to essential references\n#### We conducted a series of experiments using SwinFuse, with the corresponding results presented in [NewFig1](https://anonymous.4open.science/r/TG-ECNet/NewFig1.jpg), [Newfig2](https://anonymous.4open.science/r/TG-ECNet/NewFig2.png), [NewFig3](https://anonymous.4open.science/r/TG-ECNet/NewFig3.png), [NewFig4](https://anonymous.4open.science/r/TG-ECNet/NewFig4.png), [NewFig5](https://anonymous.4open.science/r/TG-ECNet/NewFig5.png) and [Newfig6](https://anonymous.4open.science/r/TG-ECNet/NewFig6.png). Our method outperformed MGDN across all tasks, and we have also included this literature in our references.\n---\n\n## Response to limited ablation studies on critical components\n#### in [NewFig7](https://anonymous.4open.science/r/TG-ECNet/NewFig7.png), we have conducted two other ablation experiments. First, since we use a learnable mask in the fusion stage, we compare this setting with visible modality dominant(VIS Major), infrared modality dominant(IR Major), and direct VIS-IR modality addition(plus). The result demonstrates that our setting is suitable for different scenarios. Second, since we use the multi-expert collaboration module in both stages, we conduct experiments in which only one stage uses the multi-expert collaboration module. In the experiments, we demonstrate that the module is instrumental in both stages.\n---\nThanks for your suggestions.\n\n[r1] Li, et al. AWFusion. arXiv:2402.02090,2024.\n\n[r2] Guan, et al. Mutual-guided dynamic network for image fusion. ACM MM, 2023.\n\n[r3] Wang, et al. SwinFuse. IEEE Transactions on Instrumentation and Measurement, 2022.", "claims_and_evidence": "The claims made in the paper are well-supported by both quantitative and qualitative evidence. The authors provide:\n\n- Quantitative Results: Metrics such as CC (Correlation Coefficient), MSE (Mean Squared Error), PSNR are used to evaluate the performance of TG-ECNet on synthetic and real-world datasets. The results consistently show that TG-ECNet outperforms state-of-the-art methods across various degradation scenarios.\n\n- Qualitative Results: Visual comparisons demonstrate that TG-ECNet effectively removes noise, haze, and stripe noise while preserving fine details and improving fusion quality. The fusion results are clearer and more informative compared to other methods.\n\n- Downstream Task Evaluation: The authors evaluate the impact of TG-ECNet on object detection tasks using YOLOv5, showing that the fused images generated by TG-ECNet lead to higher detection accuracy (mAP and AP(0.5:0.95)) compared to other methods.\n\nHowever, there are a few areas where the evidence could be strengthened:\n\n- Real-World Generalization: While the synthetic dataset is well-constructed, the evaluation on real-world data is limited. More diverse real-world scenarios (e.g., outdoor scenes, different lighting conditions) should be tested to further validate the robustness of TG-ECNet.", "methods_and_evaluation_criteria": "The proposed methods and evaluation criteria are appropriate for the problem at hand. The use of degradation-aware gating to dynamically adapt to different degradation types and fusion-aware gating to selectively aggregate multimodal features is well-motivated and effectively addresses the ambiguity of degraded image fusion.", "theoretical_claims": "The paper does not make strong theoretical claims, so there are no theoretical proofs to evaluate. The focus is primarily on the empirical validation of the proposed method.", "experimental_designs_or_analyses": "The experimental design is sound, with both synthetic and real-world evaluations. The synthetic dataset is well-constructed, and the real-world experiments demonstrate the practical utility of TG-ECNet. However, there are a few areas where the experimental analysis could be improved:\n\n- Real-World Data Diversity: The real-world experiments are limited to a few scenarios. Testing on a wider range of real-world data (e.g., outdoor scenes, different flicker frequencies) would strengthen the claims of generalizability.\n\n- Downstream Task Evaluation: While the paper shows improvements in object detection, it would be beneficial to evaluate the impact of TG-ECNet on other downstream tasks (e.g., segmentation, SLAM) to further demonstrate the versatility of the framework.", "relation_to_broader_scientific_literature": "The paper is well-situated within the broader literature on multimodal image fusion and degraded image restoration. The authors discuss related works in both conventional video deflickering and event signal filtering, highlighting the unique challenges posed by degraded multimodal images. The proposed method builds on existing ideas (e.g., attention mechanisms, spatio-temporal modeling) but introduces novel components (e.g., task-gated router) to address the specific problem of degraded image fusion.", "essential_references_not_discussed": "The paper covers most of the relevant literature, but there are a few areas where additional references could strengthen the discussion:\n\n- Transformer-Based Fusion: The paper could discuss more recent transformer-based approaches for multimodal image fusion (e.g., SwinFuse, TransFuse) in more detail, as these methods are relevant to the problem of degraded image fusion.", "other_strengths_and_weaknesses": "- Limited Real-World Evaluation: The real-world experiments are limited in scope, and more diverse scenarios should be tested to validate the generalizability of TG-ECNet.", "other_comments_or_suggestions": "- Expand Ablation Study: The ablation study could be expanded to include more variations of the network architecture to better understand the contribution of each component.", "questions_for_authors": "Please refer to the above parts.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "EYvxyyT7V9", "reviewer": "Reviewer_5m4E", "summary": "In this paper, authors proposes a framework, TG-ECNet, for degraded multi-modal fusion by unifying restoration and fusion. The key design involves task-gated routing and expert collaboration. The paper conduct a series of experiments to demonstrate the effectiveness of TG-ECNet.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for recognizing our contributions in this work. We will reply to your questions in order.\n\n---\n\n## Response to the essential references\n#### Concerning the references you suggested, we will incorporate them into our bibliography. Furthermore, our experimental configuration utilizes state-of-the-art image restoration algorithms, AdaIR (ICLR 2025), for preprocessing purposes.\n--- \n\n## Response to the doubts towards our two-stage method\n#### The traditional two-stage approach (image restoration followed by fusion) may be contradictory. Image restoration tends to eliminate noisy information while image fusion tends to integrate more valid information, but the restoration model may eliminate information of interest for image fusion in the first stage, leading to sub-optimal fusion performance. In our work, the proposed unified framework realizes the divide-and-conquer treatment of different degradation tasks by arranging the corresponding experts to process dynamically through Degradation-Aware Gating performing task routing, while the multi-expert system guided by Fusion-Aware Gating dynamically equilibrates the degree of information retention between the fusion and restoration tasks to realize the better restoration and fusion results.\n#### In comparative evaluations, even when incorporating state-of-the-art network AdaIR (ICLR 2025) for preprocessing and advanced fusion algorithms in cascaded networks ([NewFig3](https://anonymous.4open.science/r/TG-ECNet/NewFig3.png)). Such a setting still underperforms our approach. Our method demonstrates significant advantages through comprehensive experimental validation. Primarily, it effectively minimizes feature loss induced by network cascading while fully exploiting the shared characteristics between image fusion and restoration tasks. \n#### Furthermore, to ensure rigorous validation, we conducted generalization tests using non-degraded images, which consistently showed that dual-network architectures achieve suboptimal performance compared to our method. These results collectively highlight the technical superiority and robustness of our proposed framework.\n---\n\n## Response to the \"Degradation-Aware Gating\" and \"Task-Gated Router\"\n#### The \"Task-Gated Router\" in Fig. 3 is an internal component of the \"Degradation-Aware Gating\" and \"Fusion-Aware Gating\" in Fig. 2. The Task-Gated Router operates by dynamically integrating input features with task-specific prompt components to generate adaptive routing weights, effectively bridging the gap between task requirements and feature representation. \n#### Meanwhile, the \"Degradation-Aware Gating\" and \"Fusion-Aware Gating\" modules work in tandem to perform intelligent expert gating, where the computed weights selectively filter and combine the outputs from different experts. This dual-gating design not only maintains task-relevant feature propagation but also ensures optimal expert utilization based on both degradation characteristics and fusion objectives, thereby enhancing the model's capability to handle complex multi-task scenarios.\n---\n\n## Response to the U-shaped Transformer network\n#### The two U-shaped Transformer networks shown in Fig. 2 share identical parameters. During the first-stage training, we developed a base network capable of simultaneously handling degradations in both visible and infrared modalities. It should be noted that such network architecture is quite common in image restoration tasks and does not represent the core innovation of our work. We have added the details of the model architecture in the revised manuscript.\n---\n\n## Response to Questions\n#### 1. We apologize for the unclear presentation. First, the loss of Stage 1 is noted in Stage 2 in Fig.2. Second, in the visible-infrared image fusion task, we do not have the ground truth $I_{F}^{C} $.\n#### 2. We thank the reviewers for pointing out the difference in bolding, which we will correct in the revised manuscript.\n#### 3. The components you mentioned are all integral parts of the MoE system. The \"Prompt Components\" are derived by compressing features into task-specific prompts that encapsulate relevant task information. The ''weight'' is used to combine the outputs from different experts.\n#### 4. Thanks for correcting this typo. In our work, we select the top 6 experts from 11 experts to cope with different degradations.\n#### 5. These five metrics are all classic evaluation indicators in image fusion. Since our method focuses on utilizing expert systems to identify and eliminate degradations rather than directly removing degradations through text prompts and LLMs like Text-IF, it is essential for us to compute PSNR, a metric shared by both image restoration and fusion tasks. This calculation demonstrates the robustness of our experimental results. Also, the other four metrics are commonly adopted in other fusion algorithms as well. \n\n---\n\nThanks for your suggestions.", "claims_and_evidence": "Yes, the claims is supported by evidence.", "methods_and_evaluation_criteria": "Yes, it makes sense for the problem.", "theoretical_claims": "There are not theoretical claims in the paper.", "experimental_designs_or_analyses": "I have checked the experimental designs. There are some problems and please see more details in the following questions.", "relation_to_broader_scientific_literature": "The key contributions of the TG-ECNet framework are connected to advancements in three areas of the broader literature: multi-modal image fusion, degradation-aware restoration, and dynamic network architectures.", "essential_references_not_discussed": "The key references for image restoration are not included in Sec. 2.2, such as Restormer[1], Prompt IR[2], DGUNet[3], etc. \n\n[1] Zamir, Syed Waqas, et al. \"Restormer: Efficient transformer for high-resolution image restoration.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.\n\n[2] Potlapalli, Vaishnav, et al. \"Promptir: Prompting for all-in-one image restoration.\" Advances in Neural Information Processing Systems 36 (2023): 71275-71293.\n\n[3] Mou, Chong, Qian Wang, and Jian Zhang. \"Deep generalized unfolding networks for image restoration.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.", "other_strengths_and_weaknesses": "Strengths:\n1. A novel framework to unifiy image restoration and fusion via a task-gated router and multi-expert collaboration.\n2. Extensive experiments by authors.\n3. The framework demonstrates competitive performance under four degradation scenarios (noise, haze, blur, stripe noise).\n4. The paper also conducts experiments on object detection. \n\n---\nWeaknesses:\n1. This work is still a two-stage method, where the first stage is to restore clean visible images and clean infrared images, and the second stage is to perform image fusion. So why don't I use advanced image restoration algorithms for the first stage? Then, I only need to care about the fusion.\n2. How does the \"Degradation-Aware Gating\" in Figure 2 for the degraded visible and infrared image work? Is this the same as \"Task-Gated Router\" in Figure 3?\n3. According to Figure 2, two U-shaped Transformer network are used for visible and infrared images, respectively. Is this understanding right? The details about this Transformer network are missing.", "other_comments_or_suggestions": "Please polish this paper and then seek for possible publication. The current version is somewhat coarse.", "questions_for_authors": "1. There are some errors in Figure 1. From Figure 1, the \"Train Stage 1\" doesn't involve the loss. But according to Sec. 3.3, the first training stage optimizes the parameters through the restoration loss. Then the fused image ground truth $\\boldsymbol{I^C_F}$ is missing in the figure. \n2. Again for Figure 1, the symbols are not consistent with the text parts. For example, degraded input $\\boldsymbol{I^d_V}$,  $\\boldsymbol{I^d_I}$ in Figure 1 and $I^d_V$, $I^d_I$ in Sec. 3.3.\n3. For Figure 2, what does \"Prompt Components\" mean? It is not mentioned in any sections. Then, what do the dotted line and solid line from \"Weights\" represent?\n4. In Sec. 4.1 \"Experimental Setting\", \"the number of experts K and the hyperparameter l balance were heuristically set to 2 and 0.0001 respectively.\" What is the hyperparameter l, which can not be found in Method. Then, why K is set as 2, where there are at least four degradations involved in the paper.\n5. For evaluation metrics, why choosing CC, MSE, PSNR, $N_{abf}$ and MS-SSIM, what do these mean and how they are calculated? In Text-IF and EMMA, they both apply EN, SD, VIF and SCD.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: The final scores for this submission are 2 accepts and 2 weak accepts, showing a consistent and generally positive assessment. All reviewers chose to raise their ratings after the rebuttal phase, with most indicating that the authors had successfully addressed their initial concerns. In light of these considerations, I believe this work, with some further refinement, is capable of meeting the standards of ICML 2025, and I am therefore inclined to recommend acceptance.
Decision: Accept (poster)