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An Optical Flow-Based Approach for Minimally-Divergent Velocimetry Data Interpolation

Research paper by Berkay Kanberoglu, Dhritiman Das, Priya Nair, Pavan Turaga, David Frakes

Indexed on: 20 Dec '18Published on: 20 Dec '18Published in: arXiv - Computer Science - Computer Vision and Pattern Recognition



Abstract

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.