Learn to Search a Lightweight Architecture for Target-Aware Infrared and Visible Image Fusion

Liu, Jinyuan; Wu, Yuhui; Wu, Guanyao; Liu, Risheng; Fan, Xin

doi:10.1109/lsp.2022.3180672

Cited by 16 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the powerful nonlinear fitting capabilities, neural networks have been widely applied in infrared and visible image fusion, achieving performance far superior to traditional methods. Currently, the methods of infrared and visible image fusion based on deep learning can generally be divided into four types: CNN-based methods [ 33 , 34 , 35 , 36 ], GAN-based methods [ 37 , 38 , 39 , 40 , 41 ], AE-based [ 1 , 42 , 43 , 44 , 45 ] methods, and transformer-based [ 32 , 46 , 47 , 48 , 49 ] methods. CNN-based methods tend to focus on the design of loss functions, forcing the model to generate images that contain as much information from the source images as possible.…”

Section: Related Workmentioning

confidence: 99%

FERFusion: A Fast and Efficient Recursive Neural Network for Infrared and Visible Image Fusion

Yang,

Xiang,

Chen

et al. 2024

Sensors

View full text Add to dashboard Cite

The rapid development of deep neural networks has attracted significant attention in the infrared and visible image fusion field. However, most existing fusion models have many parameters and consume high computational and spatial resources. This paper proposes a fast and efficient recursive fusion neural network model to solve this complex problem that few people have touched. Specifically, we designed an attention module combining a traditional fusion knowledge prior with channel attention to extract modal-specific features efficiently. We used a shared attention layer to perform the early fusion of modal-shared features. Adopting parallel dilated convolution layers further reduces the network’s parameter count. Our network is trained recursively, featuring minimal model parameters, and requires only a few training batches to achieve excellent fusion results. This significantly reduces the consumption of time, space, and computational resources during model training. We compared our method with nine SOTA methods on three public datasets, demonstrating our method’s efficient training feature and good fusion results.

show abstract

Section: Related Workmentioning

confidence: 99%

FERFusion: A Fast and Efficient Recursive Neural Network for Infrared and Visible Image Fusion

Yang,

Xiang,

Chen

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Specifically, the proposed strategy is developed on differentiable architecture search (DARTS) (Liu, Simonyan, and Yang 2018;Liu et al 2022aLiu et al , 2021Liu et al , 2023a. The differentiable search strategy relaxes the discrete search space into a continuous one by introducing the continuous relaxation α, and the whole optimization objective for search can be formulated as:…”

Section: Adaptive Adversarial Trainingmentioning

confidence: 99%

“…While deep learning based image stitching works have achieved significant advancements, their robustness against adversarial attacks remains a concern. Despite their imperceptibility to the human vision, subtle perturbations can drastically modify the predicted results (Liu et al 2023d). Given the complexity and diversity of real-world scenes, the imperceptible perturbations can easily blend into the detailed content.…”

Section: Introductionmentioning

confidence: 99%

Towards Robust Image Stitching: An Adaptive Resistance Learning against Compatible Attacks

Jiang,

Li,

Liu

et al. 2024

AAAI

View full text Add to dashboard Cite

Image stitching seamlessly integrates images captured from varying perspectives into a single wide field-of-view image. Such integration not only broadens the captured scene but also augments holistic perception in computer vision applications. Given a pair of captured images, subtle perturbations and distortions which go unnoticed by the human visual system tend to attack the correspondence matching, impairing the performance of image stitching algorithms. In light of this challenge, this paper presents the first attempt to improve the robustness of image stitching against adversarial attacks. Specifically, we introduce a stitching-oriented attack (SoA), tailored to amplify the alignment loss within overlapping regions, thereby targeting the feature matching procedure. To establish an attack resistant model, we delve into the robustness of stitching architecture and develop an adaptive adversarial training (AAT) to balance attack resistance with stitching precision. In this way, we relieve the gap between the routine adversarial training and benign models, ensuring resilience without quality compromise. Comprehensive evaluation across real-world and synthetic datasets validate the deterioration of SoA on stitching performance. Furthermore, AAT emerges as a more robust solution against adversarial perturbations, delivering superior stitching results. Code is available at: https://github.com/Jzy2017/TRIS.

show abstract

SimpliFusion: a simplified infrared and visible image fusion network

Liu,

Li,

Liu

et al. 2024

Vis Comput

View full text Add to dashboard Cite

Learn to Search a Lightweight Architecture for Target-Aware Infrared and Visible Image Fusion

Cited by 16 publications

References 26 publications

FERFusion: A Fast and Efficient Recursive Neural Network for Infrared and Visible Image Fusion

FERFusion: A Fast and Efficient Recursive Neural Network for Infrared and Visible Image Fusion

Towards Robust Image Stitching: An Adaptive Resistance Learning against Compatible Attacks

SimpliFusion: a simplified infrared and visible image fusion network

Contact Info

Product

Resources

About