Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Chuangeng Tian"'
Autor:
Chuangeng Tian, Lei Zhang
Publikováno v:
Frontiers in Neuroscience, Vol 18 (2024)
Multimodal medical fusion images (MMFI) are formed by fusing medical images of two or more modalities with the aim of displaying as much valuable information as possible in a single image. However, due to the different strategies of various fusion al
Externí odkaz:
https://doaj.org/article/e60d65b0cf7a488d876dc02a2ae3db22
Publikováno v:
Frontiers in Neuroscience, Vol 16 (2022)
Multimodal medical image fusion (MMIF) has been proven to effectively improve the efficiency of disease diagnosis and treatment. However, few works have explored dedicated evaluation methods for MMIF. This paper proposes a novel quality assessment me
Externí odkaz:
https://doaj.org/article/cf3c47d1fa794279befa82f9b72fb7a8
Publikováno v:
IEEE Access, Vol 7, Pp 96048-96059 (2019)
Multimodal medical image fusion (MMIF) plays critical roles in image-guided clinical diagnostics and treatment. Pulse coupled neural network (PCNN) has been applied in image fusion for several years. In the schemes of image fusion based on PCNN, the
Externí odkaz:
https://doaj.org/article/1f5018ab0ffc4f2eb7e5c180ebec5f08
Publikováno v:
International Journal of Imaging Systems and Technology
Blur is a key property in the perception of COVID‐19 computed tomography (CT) image manifestations. Typically, blur causes edge extension, which brings shape changes in infection regions. Tchebichef moments (TM) have been verified efficiently in sh
Autor:
Lei Chen, Chuangeng Tian
Publikováno v:
Wireless Networks.
In haze weather, the image contrast and clarity are often poor, which affects the visual effect of the image. Therefore, it is of great significance to study a fast and effective method to clear the haze image. This paper proposes an image dehazing f
Publikováno v:
Digital Signal Processing. 91:66-76
Recent years have witnessed the explosive growth of multimedia applications over networks and increasingly high requirements of consumers for multimedia signals in terms of quality of experience (QoE). Effective and efficient yet energy-saving salien
Publikováno v:
IEEE Access, Vol 7, Pp 96048-96059 (2019)
Multimodal medical image fusion (MMIF) plays critical roles in image-guided clinical diagnostics and treatment. Pulse coupled neural network (PCNN) has been applied in image fusion for several years. In the schemes of image fusion based on PCNN, the
Publikováno v:
Simulation Tools and Techniques ISBN: 9783030727918
SimuTools (1)
SimuTools (1)
Linear Wireless Sensor Networks (LWSN) play an important role in bridge healthy monitoring, such as vibration, deformation, stress and so on. It is basically based on the observation that energy balance and delivery rate directly determine the runnin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cde3404fd278a80180508dadcda650e7
https://doi.org/10.1007/978-3-030-72792-5_57
https://doi.org/10.1007/978-3-030-72792-5_57
Publikováno v:
Simulation Tools and Techniques ISBN: 9783030727918
SimuTools (1)
SimuTools (1)
In this paper, an improved model based on the combination of residual and inverted residual blocks is proposed for image expression recognition, named as bi-directional residual network. The main objective of the proposed method is to alleviate the p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::80d0d14a8131e82d533684ad4e069780
https://doi.org/10.1007/978-3-030-72792-5_16
https://doi.org/10.1007/978-3-030-72792-5_16
Publikováno v:
Simulation Tools and Techniques ISBN: 9783030727949
SimuTools (2)
SimuTools (2)
An improved image segmentation model was established to achieve accurate detection of target contours under high noise, low resolution, and uneven illumination environments. The new model is based on the variational level set algorithm, which improve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::da5dc9adba064dfb48c85dfea0190250
https://doi.org/10.1007/978-3-030-72795-6_55
https://doi.org/10.1007/978-3-030-72795-6_55