Attention-based hierarchical fusion of visible and infrared images

Chen, Yanfei; Sang, Nong

doi:10.1016/j.ijleo.2015.08.120

Cited by 14 publications

(4 citation statements)

References 14 publications

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“…7(a)-(b). Furthermore, the presented fusion algorithm was parallel with seven existing algorithms, which entitled, DWT [32] [33], DCT [34] [35], CT [36], NSCT [37], NSST [38] [39], NSCT-PCNN [40], NSST-PCNN [41]. The main difference among presented algorithm and other considered algorithms were revealed in Table 2.…”

Section: Fusion Results Of Presented Algorithmmentioning

confidence: 99%

A multisource image fusion method for multimodal pig-body feature detection

Zhong¹,

Wang²,

Gao³

2020

KSII TIIS

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Section: Fusion Results Of Presented Algorithmmentioning

confidence: 99%

A multisource image fusion method for multimodal pig-body feature detection

Zhong¹,

Wang²,

Gao³

2020

KSII TIIS

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“…The robotic endoscopic positioning system can provide a very clear and comprehensive surgical field of vision, allowing the doctor to perform the surgery accurately. The operating room is also equipped with 4 TV monitors to maintain direct communication with the outside world [28]. The surgeon can see the results of the pathological section on the screen, and the pathologist can realistically observe the organ tissue of the patient outside the operating room.…”

Section: ) Intelligent Operating Roommentioning

confidence: 99%

Research on Hierarchical Data Fusion of Intelligent Medical Monitoring

Xue

Lu²,

Wang³

et al. 2020

IEEE Access

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The IOT-based intelligent telemedicine system is an important direction for future development. It combines information space and physical space to inform and network people's living environment. The distribution of medical resources in China is extremely uneven. There are many excellent medical resources in big cities. The resources of small and medium-sized cities are very few, which leads to patients going to large city hospitals for medical treatment, resulting in hospital overcrowding. Intelligent telemedicine system sinks excellent medical resources. Patients can also enjoy the excellent medical resources of the big city hospitals in the local area. Therefore, the use of advanced networks and science and technology, the integration of information technology and medical technology, the establishment of intelligent telemedicine system, will certainly optimize the hospital business process. This paper proposes research on multi-level data fusion under intelligent medical monitoring, which will improve its resource utilization and work efficiency, and promote hospital hierarchical data construction. Innovative use of Internet of Things technology promotes academic exchanges among hospitals, which improves doctors' technical skills, and hospital medical standards. The development of the Internet of Things is significant to the development of China's medical industry. It is hoped that this article can help the development of telemedicine in China.

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“…In addition to aforementioned approaches, visual attention-based or saliency-based techniques are also applied in image fusion in light of human psychological observations [14][15][16][17][18]. These approaches attempt to reflect the significance of human visual attentions in performing image fusion, assuming that the fusion is to pursue an integration of visual information in the first place.…”

Section: Introductionmentioning

confidence: 99%

Image fusion via nonlocal sparse K-SVD dictionary learning

Bai

et al. 2016

Appl. Opt.

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Image fusion aims to merge two or more images captured via various sensors of the same scene to construct a more informative image by integrating their details. Generally, such integration is achieved through the manipulation of the representations of the images concerned. Sparse representation plays an important role in the effective description of images, offering a great potential in a variety of image processing tasks, including image fusion. Supported by sparse representation, in this paper, an approach for image fusion by the use of a novel dictionary learning scheme is proposed. The nonlocal self-similarity property of the images is exploited, not only at the stage of learning the underlying description dictionary but during the process of image fusion. In particular, the property of nonlocal self-similarity is combined with the traditional sparse dictionary. This results in an improved learned dictionary, hereafter referred to as the nonlocal sparse K-SVD dictionary (where K-SVD stands for the K times singular value decomposition that is commonly used in the literature), and abbreviated to NL_SK_SVD. The performance of the NL_SK_SVD dictionary is applied for image fusion using simultaneous orthogonal matching pursuit. The proposed approach is evaluated with different types of images, and compared with a number of alternative image fusion techniques. The resultant superior fused images using the present approach demonstrates the efficacy of the NL_SK_SVD dictionary in sparse image representation.

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Attention-based hierarchical fusion of visible and infrared images

Cited by 14 publications

References 14 publications

A multisource image fusion method for multimodal pig-body feature detection

A multisource image fusion method for multimodal pig-body feature detection

Research on Hierarchical Data Fusion of Intelligent Medical Monitoring

Image fusion via nonlocal sparse K-SVD dictionary learning

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