文件名称:ltamebsolve
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In this paper, we propose a multi-sensor super-resolution framework for hybrid imaging to super-resolve
data one modality by taking advantage of additional guidance images of a complementary modality.
This concept is applied to hybrid 3-D range imaging in image-guided surgery, where high-quality photomet-
ric data is exploited to enhance range images of low spatial resolution. We formulate super-resolution based
on the maximum a-posteriori (MAP) principle a-In this paper, we propose a multi-sensor super-resolution framework for hybrid imaging to super-resolve
data one modality by taking advantage of additional guidance images of a complementary modality.
This concept is applied to hybrid 3-D range imaging in image-guided surgery, where high-quality photomet-
ric data is exploited to enhance range images of low spatial resolution. We formulate super-resolution based
on the maximum a-posteriori (MAP) principle a
data one modality by taking advantage of additional guidance images of a complementary modality.
This concept is applied to hybrid 3-D range imaging in image-guided surgery, where high-quality photomet-
ric data is exploited to enhance range images of low spatial resolution. We formulate super-resolution based
on the maximum a-posteriori (MAP) principle a-In this paper, we propose a multi-sensor super-resolution framework for hybrid imaging to super-resolve
data one modality by taking advantage of additional guidance images of a complementary modality.
This concept is applied to hybrid 3-D range imaging in image-guided surgery, where high-quality photomet-
ric data is exploited to enhance range images of low spatial resolution. We formulate super-resolution based
on the maximum a-posteriori (MAP) principle a
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