An Optical Flow-Based Approach for Minimally-Divergent Velocimetry Data Interpolation
Three-dimensional (3D) biomedical image sets are often acquired with in-plane pixel spacings that are far less than the out-of-plane spacings between images. The resultant anisotropy, which can be detrimental in many applications, can be decreased using image interpolation. Optical flow and/or other...
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| creator | Kanberoglu, Berkay Das, Dhritiman Nair, Priya Turaga, Pavan Frakes, David |
| description | Three-dimensional (3D) biomedical image sets are often acquired with in-plane
pixel spacings that are far less than the out-of-plane spacings between images.
The resultant anisotropy, which can be detrimental in many applications, can be
decreased using image interpolation. Optical flow and/or other
registration-based interpolators have proven useful in such interpolation roles
in the past. When acquired images are comprised of signals that describe the
flow velocity of fluids, additional information is available to guide the
interpolation process. In this paper, we present an optical-flow based
framework for image interpolation that also minimizes resultant divergence in
the interpolated data. |
| doi_str_mv | 10.48550/arxiv.1812.08882 |
| format | Article |
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pixel spacings that are far less than the out-of-plane spacings between images.
The resultant anisotropy, which can be detrimental in many applications, can be
decreased using image interpolation. Optical flow and/or other
registration-based interpolators have proven useful in such interpolation roles
in the past. When acquired images are comprised of signals that describe the
flow velocity of fluids, additional information is available to guide the
interpolation process. In this paper, we present an optical-flow based
framework for image interpolation that also minimizes resultant divergence in
the interpolated data.</description><identifier>DOI: 10.48550/arxiv.1812.08882</identifier><language>eng</language><subject>Computer Science - Computer Vision and Pattern Recognition</subject><creationdate>2018-12</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,778,883</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1812.08882$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1812.08882$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Kanberoglu, Berkay</creatorcontrib><creatorcontrib>Das, Dhritiman</creatorcontrib><creatorcontrib>Nair, Priya</creatorcontrib><creatorcontrib>Turaga, Pavan</creatorcontrib><creatorcontrib>Frakes, David</creatorcontrib><title>An Optical Flow-Based Approach for Minimally-Divergent Velocimetry Data Interpolation</title><description>Three-dimensional (3D) biomedical image sets are often acquired with in-plane
pixel spacings that are far less than the out-of-plane spacings between images.
The resultant anisotropy, which can be detrimental in many applications, can be
decreased using image interpolation. Optical flow and/or other
registration-based interpolators have proven useful in such interpolation roles
in the past. When acquired images are comprised of signals that describe the
flow velocity of fluids, additional information is available to guide the
interpolation process. In this paper, we present an optical-flow based
framework for image interpolation that also minimizes resultant divergence in
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pixel spacings that are far less than the out-of-plane spacings between images.
The resultant anisotropy, which can be detrimental in many applications, can be
decreased using image interpolation. Optical flow and/or other
registration-based interpolators have proven useful in such interpolation roles
in the past. When acquired images are comprised of signals that describe the
flow velocity of fluids, additional information is available to guide the
interpolation process. In this paper, we present an optical-flow based
framework for image interpolation that also minimizes resultant divergence in
the interpolated data.</abstract><doi>10.48550/arxiv.1812.08882</doi><oa>free_for_read</oa></addata></record> |
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| title | An Optical Flow-Based Approach for Minimally-Divergent Velocimetry Data Interpolation |
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