HDDGAN: A Heterogeneous Dual-Discriminator Generative Adversarial Network for Infrared and Visible Image Fusion
Guosheng Lu Zile Fang Chunming He Zhigang Zhao























































































[mycorrespondingauthor]Corresponding author


College of New Materials and New Energies, Shenzhen Technology University, Shenzhen, 518118, China, 

Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China.











Infrared and visible image fusion (IVIF) aims to preserve thermal radiation information from infrared images while integrating texture details from visible images, enabling the capture of important features and hidden details of subjects in complex scenes and disturbed environments. Consequently, IVIF offers distinct advantages in practical applications such as video surveillance, night navigation, and target recognition. However, prevailing methods often face challenges in simultaneously capturing thermal region features and detailed information due to the disparate characteristics of infrared and visible images. Consequently, fusion outcomes frequently entail a compromise between thermal target area information and texture details. In this study, we introduce a novel heterogeneous dual-discriminator generative adversarial network (HDDGAN) to address this issue. Specifically, the generator is structured as a multi-scale skip-connected codec, facilitating the extraction of essential features from different source images. To enhance the information representation ability of the fusion result, an attention mechanism is employed to construct the information fusion layer within the generator, leveraging the disparities between the source images. Moreover, recognizing the distinct learning requirements of information in infrared and visible images, we design two discriminators with differing structures. This approach aims to guide the model to learn salient information from infrared images while simultaneously capturing detailed information from visible images. Extensive experiments conducted on various public datasets demonstrate the superiority of our proposed HDDGAN over other state-of-the-art (SOTA) algorithms, highlighting its enhanced potential for practical applications.




Infrared and visible imageGenerative adversarial networkAttention mechanism.






§ INTRODUCTION




With the advancement of optics and laser technology, an increasing number of scholars are exploring its potential applications in engineering <cit.>, particularly in interdisciplinary fields related to information technology. Among these domains, information perception technology has emerged as a prominent research direction <cit.>. Information perception entails the acquisition of valuable data through optical sensor systems such as cameras and lidars <cit.>. In practical scenarios, a single type of sensor is typically employed for data collection. However, due to the limited perspective provided by a single sensor and susceptibility to data capture interference, crucial information may be overlooked, significantly limiting the potential applications of perception technologies. Enhancing optical sensors <cit.> or integrating images from various sensors <cit.> can effectively mitigate this challenge. Nonetheless, compared to hardware enhancements, sensor image fusion represents a more cost-effective and user-friendly solution. This approach can extract independent and crucial information not obtainable by a single sensor by leveraging the complementary properties of different optical sensors.

Numerous sensor image fusion techniques exist, including Radar and Lidar fusion <cit.>, radar and visible fusion <cit.>, and infrared and visible image fusion (IVIF) <cit.>, among others. IVIF, in particular, offers superior complementarity to images produced by both sensors. Notably, the imaging mechanisms differ for infrared and visible images. Infrared sensors capture thermal radiation information emitted by objects, effectively highlighting significant target areas even in challenging environmental conditions such as exposure, low illumination, and occlusion. However, these images typically exhibit lower spatial resolution. In contrast, visible sensors acquire reflected light information, yielding images with higher spatial resolution capable of characterizing effective information in complex scenes. Nevertheless, visible images are susceptible to changes in external conditions such as weather and illumination. Fusion of these two image types can effectively compensate for their respective deficiencies, enabling fused results to convey rich and crucial information in harsh environments and complex scenes. Fig. <ref> illustrates the benefits of IVIF, underscoring its high potential for practical engineering applications <cit.>.



In recent decades, a plethora of approaches to infrared and visible image fusion (IVIF) have emerged, typically classified into two categories: traditional model-based methods and deep learning-based approaches.

Traditional methods commonly fall into four categories: multi-scale transform methods (MST), such as laplacian pyramid (LP) <cit.> and complex wavelet transform (DTCWT) <cit.>; sparse representation (SR), including robust SR <cit.> and nonnegative SR <cit.>; subspace methods like principal component analysis (PCA) <cit.>; and hybrid techniques <cit.>. Despite their ability to yield satisfactory results, traditional methods suffer from inherent limitations. Specifically, manually designed feature extraction operators and fusion rules often fail to comprehensively capture image features, leading to overly complex models. 

Conversely, deep learning has gained prominence in IVIF tasks due to the robust data representation capabilities of neural networks. Primary deep learning-based fusion methods include convolutional neural network (CNN) models like U2Fusion <cit.> and IFCNN <cit.>; auto-encoder (AE) architectures such as Densefuse <cit.> and UNFusion <cit.>; and generative adversarial network (GAN) approaches <cit.>. However, CNN and AE-based methods often struggle to accurately estimate probability distributions in the absence of ground truth or standard fused images.

In the realm of GAN-based fusion algorithms, Ma et al. proposed FusionGAN <cit.> to address these challenges. Subsequently, they introduced DDcGAN <cit.> tailored for the multi-source fusion domain, garnering significant attention. Building on this foundation, Zhang et al. developed GAN-FM <cit.>, Li et al. designed MgAN-Fuse <cit.>, and Liu et al. created TarDAL <cit.>, all demonstrating remarkable fusion performance. Nevertheless, these models exhibit certain limitations. Firstly, they often focus solely on reducing information loss in the generator module, neglecting effective integration of extracted information, leading to disjointed features post-fusion. Secondly, they inadequately address differences between source images, with the dual-discriminator sharing the same structure. Consequently, models struggle to learn features from distinct sources simultaneously, resulting in the loss of thermal radiation or detailed information.

To overcome these limitations, this paper proposes a heterogeneous dual-discriminator GAN model (HDDGAN). Firstly, to address the first shortcoming, we introduce a multi-scale skip-connected structure into the generator, enhancing information utilization. Additionally, we design a fusion strategy employing an attention network to assign weights to features based on pixel differences, enabling effective fusion and information characterization. Secondly, to address the weaknesses of the dual-discriminator structure, we devise two distinct discriminator networks tailored to infrared and visible image features. A global discriminator focuses on image region information to learn thermal region features, while a Markovian discriminator emphasizes local detail information to learn texture details in visible images.

The contributions of this work are summarized below:




  (1)
The generator module is structured as a multi-scale skip-connected codec to mitigate information loss. Building upon this architecture, we introduce a novel attention-based fusion strategy utilizing the differences between source images. This approach ensures that the fusion result effectively characterizes features learned from diverse sources.



  (2)
Two discriminators with distinct structures are devised to accommodate the specific attributes of infrared and visible images. This enables simultaneous targeted learning of thermal region and local detail information.



  (3)
Extensive experiments conducted on various public datasets demonstrate the effectiveness of our model. Furthermore, comparisons regarding degradation resistance, downstream application analyses, and extension experiments validate the significant application potential of our approach.


The subsequent sections of this paper are organized as follows: Section <ref> provides an overview of traditional IVIF technology and deep learning-based IVIF technology, with a particular focus on the application of GANs in this field. In Section <ref>, we delve into the structure of the proposed model and elaborate on its advantages. Section <ref> presents numerous experimental validations, encompassing qualitative and quantitative analyses, ablation experiments, degradation resistance comparisons, downstream application evaluations, and extended experiments. Finally, conclusions are drawn in Section <ref>.




§ RELATED WORKS






 §.§ Model-Based IVIF



Traditional IVIF methodologies prioritize the design of feature extraction operators and fusion rules to attain desired outcomes. As previously delineated, these methods can be classified into four primary groups. The first category encompasses MST-based methods, which are theoretically grounded in the notion that an image can be dissected into distinct components owing to the varied scales of different features. Satisfactory fusion results are achieved by integrating these components through designed rules. Representative studies include pyramid transform <cit.>, wavelet transform <cit.>, and curvelet transform <cit.>. The second category comprises SR methods. Given the observation that visual recognition systems process images in a sparse manner, enhancing valid information representation is feasible by learning an over-complete dictionary and characterizing image features with sparse coefficients <cit.>. The third category encompasses subspace-based fusion methods. Recognizing the redundancy present in most images, some scholars propose capturing internal structural information by mapping images to a low-dimensional subspace to effectively eliminate interference from this redundant information <cit.>. The fourth category involves hybrid methods, devised to bolster fusion performance by amalgamating the advantages of the aforementioned methods <cit.>. Additionally, novel perspectives in IVIF have emerged, such as GTF <cit.> proposed by Ma et al. in 2016, rooted in the theory of Total Variation (TV).





 §.§ Deep Learning-Based IVIF



Compared to traditional methods relying on manually designed rules, deep learning-based algorithms harness the potent fitting capability of neural networks, rendering them superior and thus garnering significant attention in the image fusion field. Presently, three dominant frameworks prevail. Within the CNN-based framework, Liu et al. introduced a multi-focus fusion strategy in 2017 <cit.>, facilitating fusion tasks by learning the mapping between source images and focus maps. Subsequently, Li et al. utilized a pre-trained ResNet-50 model to extract deep features from images <cit.>, while Zhang et al. employed sequential convolutional layers to extract features and enrich fusion results with information <cit.>. Regarding AE-based algorithms, Li et al. proposed a novel codec model in 2018 <cit.>, leveraging dense connections in the encoder to enhance information utilization. Additionally, Li et al. achieved feature reuse through a multi-scale approach by focusing on features at varying scales <cit.>. Furthermore, Wang et al. innovatively combined the strengths of these two AE models to aggregate useful features from both horizontal and vertical directions simultaneously <cit.>.

While these deep learning-based methods generally perform well, as previously discussed, their performance is limited by the absence of ground truth in the fusion domain. To address this challenge, Ma et al. introduced the GAN model in 2019 <cit.>, ushering in new ideas for IVIF. Subsequently, a series of improved methods were proposed <cit.>, detailed in the following subsections.





 §.§ Generative Adversarial Network for IVIF



Initially proposed by Goodfellow et al. in 2014, GAN operates on the principle of compelling the generator to produce desired data through adversarial learning between the constructed generator and discriminator <cit.>. In 2019, Ma et al. introduced GAN into the field of image fusion, with the proposed FusionGAN <cit.> using source images to guide model learning, thereby offering a novel approach to address the absence of ground truth images. However, FusionGAN solely evaluates generated images against visible images, causing the generator to predominantly focus on the data distribution of visible images while overlooking the characteristics of infrared images. Subsequently, in 2020, Ma et al. further proposed DDcGAN <cit.>, characterized by two discriminators with identical structures, aimed at compelling the generator to learn detailed features from visible images and thermal radiation information from infrared images. This dual-discriminator GAN algorithm tailored for the fusion domain has spurred the development of numerous new models. In 2021, Li et al. introduced AttentionFGAN <cit.>, incorporating multi-scale attention structures in both generator and discriminators to glean more information about salient regions from source images. Concurrently, Zhang et al. proposed GAN-FM <cit.>, which learns richer information through improved discriminator structures. Diverging from conventional global discriminator architectures, it integrates a Markovian discriminator to enhance focus on local image block structures. Subsequently, Liu et al. presented TarDAL in 2022 <cit.>, employing a joint training strategy for downstream detection tasks, achieving bilevel optimization between fusion and detection. Additionally, in 2023, Zhou et al. devised SDDGAN <cit.>, a semantic-supervised fusion network capable of preserving features of crucial semantic objects in images to learn richer important information. However, a common limitation observed in existing dual-discriminator GAN methods is the uniform structure shared by both discriminators, leading to a notable issue: since the imaging mechanisms of infrared and visible images differ, the contained information is distinct. Consequently, discriminators with uniform structures cannot effectively guide the generator in learning features from diverse source images in a targeted manner, resulting in inevitable loss of valuable information. To address this challenge, we propose HDDGAN, detailed in the following section.




§ METHOD






 §.§ Framework Overview



In this paper, a novel heterogeneous dual-discriminator GAN model is proposed, and the overall framework is depicted in Fig. <ref>. This model comprises two networks, the generator and the discriminators. The generator is designed as a multi-scale skip-connected codec structure to realize the information extraction, fusion, and image reconstruction. Two discriminators are defined as different structures to learn thermal region and detail information respectively, which are the salient discriminator (𝒟_𝒮) targeting infrared images and the detailed discriminator (𝒟_𝒟) focusing on visible images.

It is worth noting that due to the different structures of the two discriminators, there may be an imbalance in guiding the generator for learning. This could cause the model to overly focus on learning the features of one kind of source images while ignoring the other. However, our heterogeneous dual-discriminator structure also has the advantage that it can fully utilize the information from source images and highlight both important features and neglected details. This is beneficial to further exploring the potential of IVIF in practical applications, such as in extreme scenes like rain and fog, as well as in target surveillance tasks. Therefore, it is worth researching.







 §.§ Architecture of Generator



Inspired by <cit.>, we design the generator as a multi-scale skip-connected structure to effectively utilize image information. As depicted in Fig. <ref>, the generator comprises three sub-networks: encoder, fusion strategy (detailed in the following subsection), and decoder. The encoder and decoder sub-networks primarily consist of the convolutional block (CB), encoder block (EB), and decoder block (DB). CB contains one convolutional layer, EB denotes two convolutional operations with different kernel sizes, and DB consists of two identical convolutional layers. Specifically, after importing the source images I_ir and I_vi into the encoder sub-network, deep feature maps F_ir^k and F_vi^k with different sizes are obtained respectively through the CB, EB, and down-sampling operations. k represents the scale level of the feature maps. Subsequently, after traversing the fusion strategy sub-network, the fused deep feature F_fused^k is produced. Ultimately, the final fused image I_f is generated after a series of up-sampling and DB operations in the decoder sub-network. The sampling operations within the network are used to utilize multi-scale features, while the skip connections serve to enhance the extraction and preservation of intermediate features.







 §.§ Attention-Based Fusion Strategy




The information in infrared and visible images is fundamentally different, so it is a noteworthy challenge to effectively fuse this information, particularly the important thermal radiation and critical detail information. And existing dual-discriminator GAN models use infrared and visible images to guide the generator to learn features separately, so the potential imbalance in representations that may arise after fusing the separately learned information needs to be considered simultaneously. To address these issues, we develop an attention-based fusion strategy (AFS) as the information fusion layer to integrate the information rationally and effectively. The detailed structure is shown in Fig. <ref>.

Referring to the regions boxed in Fig. <ref>, it is evident that the larger the difference of these corresponding regions between source images, the more important the information specific to the respective source image. Hence, it is logical to denote the varying importance of this information based on the distance between source images, and subsequently assign corresponding weights. However, considering that thermal radiation data in infrared images are expressed as pixel intensities, while texture details in visible images are presented through gradients, the functions are following:


    μ ^k  = F_ir^k - F_vi^k/Rp( GMP( F_ir^k - F_vi^k))


    σ ^k = ∇( F_vi^k) - ∇( F_ir^k)/Rp( GMP( ∇( F_vi^k) - ∇( F_ir^k)))

where ∇(·) is the Sobel gradient operator known for its better ability to resist interference. Rp ( · ) and GMP ( · ) donate repeat and global max pooling, respectively. Following these computations, both μ ^k and σ ^k reflect the importance of different information in feature maps, with values falling into three categories: positive, negative, and zero. The positive values indicate that the information in the subtracted image is relatively significant and need to be strengthened, the negative values need to be weakened, and the zero values remain unchanged.





Subsequently, we calculate the feature maps that can highlight the respective important information through the following formulas:

    f_ir^k = μ ^k * F_ir^k + F_ir^k


    f_vi^k = σ ^k * F_vi^k + F_vi^k

where ∗ is the multiplication operation. 

Eventually, after passing through the concatenation operation (concat ( · )) and then the convolutional layer (conv ( · )), we get fused features F_fused^k:

    F_fused^k = conv( concat( f_ir^k,f_vi^k))


The deep features maps achieved after the above processing integrate the information from different source images reasonably, and effectively relieve the problem of the unbalanced information representation in the fusion result.





 §.§ Architecture of Discriminators



In the HDDGAN proposed in this paper, the discriminator networks are composed of two parts: the salient discriminator and the detailed discriminator. The specific architecture is displayed in Fig. <ref>. The input images are first fed into the multi-scale attention modules (MS-CA and MS-SA) respectively to compute the corresponding attention maps. Then, concatenate these maps with input images accordingly and feed them into the corresponding discriminative modules (D_ir and D_vi).


1) Salient Discriminator: 
To facilitate the generator to focus on learning thermal region information from infrared images during adversarial training, the sub-network is split into two components: the multi-scale channel attention module (MS-CA) and the infrared discriminative module (D_ir). The role of D_ir is to distinguish between the fused output and the original infrared image, and it consists of five layers. The first four layers are all two identical convolution operations and the last layer comprises two fully connected operations. Upon passing through this module, the result of the discrimination (the final determine probability of the input image) is produced. Furthermore, to restrict discriminator concentrates on the most critical thermal regions in the infrared image during training, the MS-CA structure was introduced. In detail, firstly, recognizing that single-scale features may not capture all essential thermal radiation information, a multi-scale mechanism is adopted to utilize more deep features via down-sampling. Afterwards, to focus on the distribution of the overall pixels in the image, i.e., the intensity information of the thermal region, we construct the channel attention module (CA) <cit.> to calculate the weight values of different channels through global average pooling (GAP) and 1-dimensional convolution, consequently reweighting the global information of the infrared features. Then, the images are restored to their original size with up-sampling operations, and weighted maps are computed after the mapping operation (selecting the maximum value of all features in channel dimensions <cit.>). Finally, these images are concatenated to form the ultimate attention map. As we can see, our salient discriminator targeted at thermal region information in the infrared image can effectively guide the generator in learning infrared features.


2) Detailed Discriminator:
Different from the global discriminative structure used in salient discriminator, to encourage the generator to fully learn the detailed information of visible images, we adopt the Markovian discriminative network <cit.> in the visible discriminative module (D_vi). This network can classify each block of the input image, placing emphasis on local details. The detailed structure is illustrated in the figure. It comprises five convolution layers, and the output is a matrix that can be averaged to obtain the final determine probability. Additionally, in line with MS-CA, the multi-scale spatial attention module (MS-SA) is also integrated into the detailed discriminator. Differently, to better emphasize the information of important local details, the relationship between local pixels is modeled using the spatial attention module (SA) <cit.>. It can capture more relationships between pixel spaces by downscaling and upscaling the number of channels during two convolutions, thereby giving greater weight to critical spatial structure information. For this reason, the generator facilitates more efficient preservation of texture details during adversarial learning.





 §.§ Loss Function




As depicted in Fig. <ref>, our loss function is comprised of two parts: the loss function of the generator (L_G), and the loss function of discriminators (L_𝒟_𝒮 and L_𝒟_𝒟).


1) Loss Function of Generator:
In order to force the generated image to contain sufficient information from both the infrared and visible images, the loss function used to guide the training of the generator consists of the adversarial loss (L_adver) and the basic loss (L_basic):

    L_G = L_adver + α L_basic

where α is the parameter to control the two terms.

Adversarial Loss.
Considering that the generator needs to deceive both discriminators during training. the adversarial loss can be defined as follows:

    L_adver = 𝔼_I_f∼d_I_f    [ ( 𝒟_𝒮( I_f) - ϕ)^2 ] 
    
        +  𝔼_I_f∼d_I_f  [ ( 𝒟_𝒟( I_f) - ϕ)^2 ]

where 𝔼 represents the mathematical expectation, 𝒟_𝒮 and 𝒟_𝒟 stand for the two discriminators, I_f denotes the fused image and d_I_f is its distribution. Moreover, ϕ refers to a probability label (the discriminative probability of discriminators for the generated image) and is set to 1 because the generator expects the discriminators to be unable to distinguish between images.

Basic Loss.
The purpose of L_basic is to constrain the learning of valid information in source images, and is divided into two items: the learning of thermal radiation information and the learning of texture detail information:

    L_basic = L_visible + β L_infrared

where

    L_infrared = I_f - I_ir_F^2


    L_visible = ∇( I_f) - ∇( I_vi)_1

where β controls the trade-off between the infrared image and visible image,  ·_F^2 and ·_1 denote the Frobenius norm and ℓ_1 norm.


2) Loss Function of Discriminators:
The loss function of discriminators L_D in this paper is combined by the loss L_𝒟_𝒮 of the salient discriminator and the loss L_𝒟_𝒟 of the detailed discriminator. Since HDDGAN has two heterogeneous discriminators that may aggravate the imbalance conflict when the generator learns different features, we jointly train the loss of the infrared and visible discriminators instead of calculating them separately <cit.>. The weight parameter γ is introduced to allow the model to adaptively adjust the optimization to solve the training imbalance arising from the difference of the discriminative structure:

    L_D = L_𝒟_𝒟 + γ L_𝒟_𝒮

where

    L_𝒟_𝒮  = 1/2×𝔼_I_ir∼d_I_ir    [(𝒟_𝒮( I_ir)-ξ_1)^2] 
    
      + 1/2×   𝔼_I_f∼d_I_f  [ ( 𝒟_𝒮( I_f) - ξ_2)^2 ]


    L_𝒟_𝒟 = 1/2×𝔼_I_vi∼d_I_vi    [(𝒟_𝒟(I_vi)-ζ_1)^2] 
    
     + 1/2×   𝔼_I_f∼d_I_f  [ ( 𝒟_𝒟( I_f) - ζ_2)^2 ]

where I_ir denotes the infrared image and d_I_ir is its distribution, while I_vi denotes the visible image and d_I_vi is its distribution. The probability labels ξ_1 and ξ_2 are set to 1 and 0, respectively, and ζ_1 and ζ_2 are similarly set to 1 and 0, respectively. This is because the discriminators expect to distinguish the input image as accurately as possible (giving the probability value of the real image as large as possible as well as the probability value of the generated image as small as possible).





 §.§ Discussion



Significantly, since the utilization of heterogeneous discriminant structures has not been previously considered in dual-discriminator models, we contemplate justifying the proposed HDDGAN in the following three aspects:


1.
In the application domain of GAN, the dual-discriminator structure is specifically designed to address the characteristics of multiple source images in the image fusion area. Therefore, it is logical for us to devise heterogeneous discriminator networks to learn features from different source images on the basis of the existing structure through the above inference. And the study in this paper hopes to provide some inspiration.


2.
The rationality of HDDGAN is manifested in the overall performance of the model as well as the effectiveness of the key heterogeneous discriminator networks. So refer to <cit.>, the qualitative and quantitative experiments are conducted in subsection <ref> to assess the overall impact of the model. Additionally, the ablation experiments on heterogeneous discriminator networks are done in subsection <ref> for evaluation.


3.
The reasonability of the model is also reflected by its generalization capability, including its performance in practical applications and extended domains. Hence, this paper also includes experiments comparing degradation resistance, target detection, and tracking in subsection <ref> and subsection <ref>, respectively, Furthermore, fusion experiments in related medical fields are conducted in subsection <ref>.




§ EXPERIMENT






 §.§ Implementation Details





1) Datasets:
The experiments in this paper are conducted on three public datasets: RoadScene <cit.>, LLVIP <cit.>, and M^3FD <cit.>. The RoadScene dataset comprises 221 pairs of infrared-visible images depicting various traffic scenes. LLVIP contains traffic images captured in low-light conditions, from which we select 300 pairs for our experiments. Additionally, M^3FD is a newly available multi-scene multimodal dataset consisting of 300 pairs of images. We randomly partition the datasets into training and testing sets, with details presented in Table <ref>. Subsequently, we uniformly crop the training set into image blocks sized at 128 × 128 to augment the training images, resulting in a total of 13,903 images.


2) Comparative Methods:
Our method, HDDGAN, is compared with nine state-of-the-art (SOTA) methods, comprising a typical traditional method, GTF <cit.>, and eight deep learning-based methods. Among the latter, U2Fusion <cit.> and STDFusion <cit.> are CNN-based fusion models, while DeepFuse <cit.>, AUIF <cit.>, and DRF <cit.> utilize autoencoder-based networks. Additionally, FusionGAN <cit.>, GANMcC <cit.>, and GAN-FM <cit.> are GAN-based methods. All comparison methods have publicly available code, and we reference the original papers for the corresponding parameter settings. We conduct retraining and testing using the same training and test data as specified in the respective papers.


3) Evaluation Metric:
Following <cit.>, we employ six commonly used indicators to evaluate the performance of IVIF methods from multiple perspectives. These indicators include entropy (EN) <cit.>, average gradient (AG) <cit.>, spatial frequency (SF) <cit.>, feature mutual information (FMI) <cit.>, visual information fidelity (VIF) <cit.>, and universal image quality index (UIQI) <cit.>. EN measures the information richness of the fused image, while AG assesses quality based on gradients. SF reflects the rate of grayscale change in the image, and FMI calculates mutual information of image features. VIF evaluates the visual quality of the fused image, and UIQI compares image brightness. In summary, larger values for these metrics indicate better fusion effects.


4) Training Details:
We utilize TensorFlow and the Adam optimizer in our model. During the training process, the generator and discriminators are updated alternately and iteratively. Specifically, the generator is optimized twice after training the discriminators six times. We set the epochs and batch sizes to 20 and 16, respectively. Additionally, we initialize the learning rate to 2× 10^-4 for the generator and 1× 10^-4 for the discriminators. Except for GTF, which is executed on MATLAB R2023a, all other methods run on an NVIDIA RTX 2080 Ti GPU. Furthermore, the hyperparameters α, β, and γ in Equations <ref>, <ref>, and <ref> are set to 100, 5, and 5, respectively.







 §.§ Results on Public Datasets














1) Qualitative Comparisons:
To demonstrate the superiority of our model, we qualitatively assess three representative image pairs from the testing set and compare the results with nine state-of-the-art (SOTA) methods mentioned earlier. The fusion outcomes of HDDGAN and the comparative algorithms are illustrated in Figures <ref> through <ref>. The primary thermal information, depicted through bright human body regions, and textures, indicated as lane lines, license plates, and bushes, are evaluated. To enhance clarity, we zoom in on a red-framed area for the thermal targets and a blue-framed area for the texture details, placing them in one corner of the picture.

From a visual perspective, HDDGAN exhibits several unique advantages. Firstly, our method closely aligns with the requirements of IVIF by generating fused images with both infrared and visible information. Notably, the results from HDDGAN exhibit similar pixel distributions to infrared images while retaining substantial texture information from visible images. Secondly, HDDGAN excels in capturing more salient thermal region information and clearer detail information compared to other SOTA algorithms, such as pedestrians in the red boxes in Figures <ref> and <ref>, and the bushes in Figure <ref>. This advantage can be attributed to the two distinct discriminator structures built in this paper, enabling the model to learn more from source images.

Additionally, our method emphasizes critical information beneficial for subsequent practical tasks. This is owed to the addition of MS-CA and MS-SA structures in discriminators, allowing focus on learning essential information. Moreover, the AFS module enables fused images to highlight important information, as evidenced by the enhanced visibility of hidden persons in Figure <ref>, and the lane lines and license plates in Figures <ref> and <ref>.

Furthermore, among the nine SOTA methods used for comparison, GTF tends to overlook boundary information (see Figure <ref>). U2Fusion strikes a balance that does not enhance either thermal region or texture information significantly (see Figure <ref>). STDFusion and AUIF perform poorly in low-light conditions (see Figure <ref>). DeepFuse, DRF, GANMcC, and GAN-FM typically lose some important texture features of visible images (see Figure <ref>). Additionally, FusionGAN consistently fails to retain high-contrast characteristics similar to infrared images (see Figure <ref>).


2) Quantitative Comparisons:
We further conduct a quantitative comparison of test images from three datasets to assess the effectiveness of HDDGAN. Table <ref> presents the average scores for the six metrics described above, with the best values highlighted in red and the next best in blue. It is evident that HDDGAN achieves the highest scores for AG, SF, and VIF across all scenes, demonstrating its superiority in preserving gradient and texture information, and enhancing visual quality. Moreover, HDDGAN also ranks at the top for EN, FMI, and UIQI metrics in most cases, showcasing its excellence in terms of information richness, mutual information utilization, and image brightness preservation. While there are some instances where our results are suboptimal, this could be attributed to variations in scene characteristics across datasets.





 §.§ Ablation Experiment




The performance of HDDGAN hinges on the design of its generator and discriminators. The enhanced generator network enables comprehensive extraction, fusion, and reconstruction of intensity and gradient information of pixels in the image. Additionally, the proposed heterogeneous discriminator networks facilitate learning more features from the source image. In this subsection, a series of experiments are conducted to validate the efficacy of the HDDGAN structure, comprising two parts: verification of the rationality of the specific structure in the generator, and evaluation of the specific network in the discriminators.


1) Analysis of the structure of the generator:
Here the ablation experiments on the multi-scale structure, skip connections, and AFS in the generator are performed.

Ablation of Sampling. 
In the generator network, we refer to the multi-scale structure <cit.> to extract and fuse the deep features of the image. This is realized by sampling operators. So to confirm the effectiveness of this multi-scale structure, we conduct the ablation experiment of sampling operators. In this experiment, we keep other structures and settings unchanged, while the sampling operations are eliminated (w/o Sampling), i.e., the generative network becomes a dense skip connection structure. Table <ref> - Table <ref> provide a specific evaluation of the metrics. It can be seen that the multi-scale structure effectively utilizes deep information of the image, and has obvious improvement in EN, AG, SF, and VIF.











Ablation of Skip Connections. 
In order to reduce the information loss during feature extraction and reconstruction, skip connections are also employed in the generator network, so this structure needs to be validated. As indicated in Table <ref> - Table <ref>, the addition of skip connections leads to improved performance across various metrics, except for individual scenarios in UIQI. Thus, the use of skip connections is reasonable. 

Ablation of AFS. 
A novel fusion strategy AFS is also proposed in this paper. AFS can enhance the visual effect of the fused image and help to improve the efficiency of downstream tasks. Here the ablation experiment of AFS is performed, and in Table <ref> - Table <ref>, w/o AFS represents that the fusion strategy proposed is replaced by a simple overlap:

    F_fused^k = 0.5× F_ir^k + 0.5× F_vi^k


It can be seen that the proposed AFS module notably boosts the metrics including EN, AG, SF, FMI, and VIF, and also performs well in UIQI, which effectively improves the information expression capability of the fused image.


2) Analysis of the structure of discriminators:
In this analysis, we examine the impact of the attention modules MS-CA and MS-SA, as well as the heterogeneous discriminator modules 𝒟_𝒮 and 𝒟_𝒟.





Ablation of MS-CA and MS-SA. 
To prompt the model to learn more information about important thermal regions and key local details, we incorporate MS-CA and MS-SA modules. To justify the effectiveness of these modules, an ablation experiment is conducted. In Figures <ref> and <ref>, (a) and (b) represent the infrared image and visible image, respectively. In (c), the result after removing the attention modules is depicted. (d) illustrates the proposed setup of HDDGAN, which includes these attention modules. 

Observing method (c), we note that after discarding the attention modules, some thermal radiation and texture information are retained, but the thermal targets are not prominent enough, and the details lack clarity. In contrast, method (d), with the embedded MS-CA and MS-SA modules, not only retains rich information learned from the source image but also enhances the contrast and texture details. These results underscore the significance of the two attention modules in discriminators, showcasing their ability to further improve the fusion effect.





Ablation of 𝒟_𝒮 and 𝒟_𝒟. 
HDDGAN comprises two distinct discriminators 𝒟_𝒮 and 𝒟_𝒟. To elucidate the role of each discriminator and their effectiveness when applied, we conduct six sets of comparison experiments as depicted in Figures <ref> and <ref>: (a) and (b) represent the infrared and visible images, respectively; (c) illustrates the method that excludes both discriminators, utilizing only the generator G for training; (d) discards the salient discriminator, employing only 𝒟_𝒟 and G for the adversarial game; (e) incorporates detailed discriminators for both infrared and visible images; (f) removes the detailed discriminator, establishing the adversarial game solely between 𝒟_𝒮 and G; (g) utilizes the same salient discriminator for both infrared and visible images; (h) represents the structure embedded with 𝒟_𝒮 and 𝒟_𝒟, identical to the setup employed in HDDGAN. 

To facilitate clear comparisons, we zoom in on the selected thermal region information and texture detail information, respectively, as shown in the bottom red and blue boxes. Throughout the experiments, all parameters and environments remain constant, except for the variations mentioned above. Method (c) excludes discriminators and learns optimally against content loss alone, resulting in a certain loss of thermal radiation and detail information. Method (d) introduces 𝒟_𝒟 based on (c), effectively preserving the textures displayed in the blue boxes, although the infrared information remains lost. Method (e) exhibits richer details after embedding both 𝒟_𝒟 simultaneously. Method (f) adds 𝒟_𝒮 on the basis of (c), with the distribution of the fused image closer to the infrared image; however, the texture details in the blue boxes are poorly expressed. Method (g) is similar to method (e); after introducing two 𝒟_𝒮, more infrared information is presented relative to (f). However, this structure also brings an issue: persons in Figure <ref> are conversely not clear enough due to excessive brightness. Finally, method (h) shares the same structure as HDDGAN, maintaining high contrast and preserving rich texture details by utilizing both 𝒟_𝒮 and 𝒟_𝒟. These comparisons fully justify the use of these two distinct discriminators.





 §.§ Degradation Resistance Experiment




As highlighted in <cit.>, images captured by visible sensors are susceptible to environmental conditions, leading to potential visual degradation—an issue prevalent in practical application scenarios. IVIF technology emerges as a promising solution to mitigate such degradation by integrating information from infrared images. Thus, to delve deeper into the versatility of IVIF across various applications, we investigate the degradation resistance capabilities of several state-of-the-art (SOTA) algorithms in this subsection.

Following the methodology outlined in <cit.>, we introduce Gaussian noise with a variance σ of 0.03 to the visible images across all testing sets. We then evaluate the performance of various methods using four metrics on the LLVIP and M^3FD datasets. Figures <ref> and <ref> present the noise resistance performance of different algorithms, revealing the overall effectiveness of HDDGAN.









 §.§ Subsequent Application Verification




As mentioned earlier, IVIF combines thermal region information from infrared images with texture information from visible images, which is beneficial for improving the performance of visual tasks. And relative to other SOTA methods, HDDGAN can further emphasize key information. To verify this point, we apply the proposed model to object detection and visual tracking tasks and conduct comparative analyses with other algorithms. 






1) Object Detection:
Object detection is a widely employed machine vision technique <cit.> for detecting specific objects (people, cars, etc.) in images. Following <cit.>, we use the well-known general-purpose detector YOLOv5 for object detection experiments on LLVIP and M^3FD. Table <ref> reports the results of these two datasets. In this context, Precision denotes the probability of correctly detecting among all detected targets, and higher precision means more real samples are detected. Recall refers to the probability of correctly recognizing all positive samples, and a large recall means fewer targets are lost. The mean average precision (mAP) is a comprehensive consideration of Precision and Recall to evaluate the quality of the model. The value of mAP ranges from 0 to 1, and the closer the value is to 1, the better the model performs. The mAP@.5 and mAP@.95 denote the mAP values at confidence levels of 0.5 and 0.95, respectively. It is evident that the fusion results of HDDGAN excel in these metrics.






2) Visual Tracking:
Visual tracking is also an essential task in computer vision, which aims to localize the position of specific objects in videos. Referring to <cit.>, we apply the algorithm to the VOT-RGBT sub-challenge, which focuses on short-term tracking in VOT2020 to comparatively assess the impact of the fusion approach on tracking performance. The VOT-RGBT benchmark, available in <cit.>, comprises 60 video sequences with aligned infrared and visible image pairs. We select the learning adaptive discriminative correlation filter(LADCF) <cit.> and group feature selection and discriminative filter(GFSDCF) <cit.> as trackers for evaluating tracking performance. Among them, LADCF achieves the best performance in the public dataset of the VOT-ST2018 sub-challenge, while GFSDCF further eliminates redundant information based on LADCF through a group feature selection strategy. Their codes are publicly available. For quantitative analysis, we employ three metrics to evaluate tracking performance: Accuracy, Failure, and expected average overlap (EAO). Accuracy represents the average overlap between predicted bounding boxes and ground truth bounding boxes. Failure is a measure of tracker robustness. And EAO evaluates the average overlap in visual properties between the tracker and ground truth. Both Accuracy and EAO are the larger the better, while Failure is the smaller the better. The quantitative analysis is illustrated in Table <ref>. Compared with the metric values of infrared frames and visible frames, our fusion model has a positive impact on RGBT tracking. Furthermore, HDDGAN is more powerful in improving the performance of visual tracking compared to other fusion methods. 





 §.§ Extension of the Proposed HDDGAN




Since biomedical image fusion shares a similar goal with the IVIF task <cit.>, we carry out two comparative experiments to assess the ability of our algorithm for the generalization in the spatial dimension. These tasks are PET and MRI image fusion (PET-MRI) and GFP and PC image fusion (GFP-PC). The images for the first task are sourced from the Whole Brain Atlas (WBA) <cit.>, and the second task utilizes data from the ATC dataset <cit.>. Seven algorithms used for comparison, namely GFF <cit.>, PCDC <cit.>, PAPCNN <cit.>, GTF, STDFusion, AUIF, and FusionGAN. Fig. <ref> and Fig. <ref> present the qualitative results of these methods, and it is easy to find that HDDGAN has better fusion outcomes compared to other methods that may suffer from detail distortion and color component preservation challenges. Besides, Table <ref> and Table <ref> also display the average results of these methods on six metrics, confirming that HDDGAN has good generalization performance compared to other methods.









§ CONCLUSION




In this study, we propose a novel model called heterogeneous dual-discriminator GAN (HDDGAN). Thanks to these heterogeneous discriminators, our model is capable of learning more features from different source images in a targeted manner, especially the important information. Moreover, we design a fusion module named attention-based fusion strategy (AFS) within the generator, which can realize a reasonable characterization of the acquired information during the fusion process. Various experiments conducted on numerous public datasets fully demonstrate the superiority and robustness of HDDGAN, as well as its potential for practical applications. In our future work, we will further explore a more lightweight and generalized fusion model based on the strengths of HDDGAN. 




§ ACKNOWLEDGMENTS

This research was supported by Horizontal Scientific Research Project of Shenzhen Technology University (No. 20231064010094, NO. 20231064010029), Graduate School-Enterprise Cooperation Project of Shenzhen Technology University (No. XQHZ202304, XQHZ2024006).


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