Infrared and visible image fusion using dual discriminators generative adversarial networks with Wasserstein distance

Li, Jing; Huo, Hongtao; Liu, Kejian; Li, Chang

doi:10.1016/j.ins.2020.04.035

Cited by 71 publications

(17 citation statements)

References 28 publications

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“…Ma et al proposed FusionGan for infrared-visible image fusion to solve the problem of missing real labels [22]. Many GAN variants have been subsequently used for image fusion to address the difficulty of GAN training [51][52][53]. The discriminator is also changed from one to multiple to preserve more information [52,54].…”

Section: B Infrared and Visible Image Fusion Based On Deep Learning M...mentioning

confidence: 99%

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Deep Unfolding Network for Multi-Band Images Synchronous Fusion

Lin

Lu³

et al. 2023

IEEE Access

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This study proposes a new deep neural network to solve the multi-band image synchronous fusion problem (MBF-Net). Unlike other deep learning-based methods, our network architecture design combines the ideas of model-driven and data-driven methods, so it is more interpretable. First, a new multiband image synchronous fusion model is proposed. The source image in the data fidelity terms and the prior regularization are implicitly represented by the deep learning network and jointly learned from the training data. The proposed model is then solved using a half quadratic splitting (HQS) algorithm and unfolded into a deep fusion network. In addition, a new saliency loss function is proposed to retain thermal radiation information to enhance the fusion effect. Finally, the experimental results on the TNO dataset demonstrated the effectiveness of the proposed MBF-Net.INDEX TERMS Image fusion, multi-band images, total variation, optimization model.

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Section: B Infrared and Visible Image Fusion Based On Deep Learning M...mentioning

confidence: 99%

“…Many GAN variants have been subsequently used for image fusion to address the difficulty of GAN training [51][52][53]. The discriminator is also changed from one to multiple to preserve more information [52,54]. The attention mechanism is widely used for infrared and visible image fusion.…”

Section: B Infrared and Visible Image Fusion Based On Deep Learning M...mentioning

confidence: 99%

Deep Unfolding Network for Multi-Band Images Synchronous Fusion

Lin

Lu³

et al. 2023

IEEE Access

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“…Furthermore, for the hybrid schemes [ 21 , 22 , 23 , 24 ] and other novel methods [ 15 , 26 , 39 ], the former combines the advantages of various algorithms while the latter adopts some uncommon but novel strategies. In [ 22 ], the simple mean filter has been used to perform a two-layer decomposition while the visual saliency detection using mean and median filters is obtained to construct the saliency and weight maps.…”

Section: Related Workmentioning

confidence: 99%

Fusion of Infrared and Visible Images Using Fast Global Smoothing Decomposition and Target-Enhanced Parallel Gaussian Fuzzy Logic

Duan

Xing

Liu

et al. 2021

Sensors

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As a powerful technique to merge complementary information of original images, infrared (IR) and visible image fusion approaches are widely used in surveillance, target detecting, tracking, and biological recognition, etc. In this paper, an efficient IR and visible image fusion method is proposed to simultaneously enhance the significant targets/regions in all source images and preserve rich background details in visible images. The multi-scale representation based on the fast global smoother is firstly used to decompose source images into the base and detail layers, aiming to extract the salient structure information and suppress the halos around the edges. Then, a target-enhanced parallel Gaussian fuzzy logic-based fusion rule is proposed to merge the base layers, which can avoid the brightness loss and highlight significant targets/regions. In addition, the visual saliency map-based fusion rule is designed to merge the detail layers with the purpose of obtaining rich details. Finally, the fused image is reconstructed. Extensive experiments are conducted on 21 image pairs and a Nato-camp sequence (32 image pairs) to verify the effectiveness and superiority of the proposed method. Compared with several state-of-the-art methods, experimental results demonstrate that the proposed method can achieve more competitive or superior performances according to both the visual results and objective evaluation.

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“…After that, they utilized GAN to preserve rich spectral information in remote sensing images (Ma et al, 2020). Li et al (2020) proposed a dual discriminator generative adversarial network to keep more details and textures in the fused image. However, GAN methods have weaknesses on the balance of generator and discriminator.…”

Section: Introductionmentioning

confidence: 99%

Infrared and visible image fusion based on particle swarm optimization and dense block

2022

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Infrared and visible image fusion aims to preserve essential thermal information and crucial visible details from two types of input images to generate an informative fusion image for better visual perception. In recent years, several hybrid methods have been applied in the field of infrared and visible image fusion. In this paper, we proposed a novel image fusion method based on particle swarm optimization and dense block for visible and infrared images. Particle swarm optimization is utilized to optimize the weighting factors of the coefficients obtained by discrete wavelet transform, then the coefficients are fused with the optimum weight to obtain the initial fusion image. The final fusion image is created by integrating the first fused image with the input visible image using a deep learning model, in which dense block is utilized for better feature extraction ability. The results of comparison experiments demonstrate that our method produces fusion images with richer details and texture features, and the fused image reduces the artifacts and noise.

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Infrared and visible image fusion using dual discriminators generative adversarial networks with Wasserstein distance

Cited by 71 publications

References 28 publications

Deep Unfolding Network for Multi-Band Images Synchronous Fusion

Deep Unfolding Network for Multi-Band Images Synchronous Fusion

Fusion of Infrared and Visible Images Using Fast Global Smoothing Decomposition and Target-Enhanced Parallel Gaussian Fuzzy Logic

Infrared and visible image fusion based on particle swarm optimization and dense block

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