2018/12/20 by Berkay Kanberoglu, Dhritiman Das, Kanberoglu, Berkay +8
Computer Science · Engineering · #Advanced Image Processing Techniques #Advanced Vision and Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Processing Techniques and Applications #Image and Signal Denoising Methods
paper · pdf · doi:10.48550/arxiv.1812.08882
openalex publication_date 2018/12/20 · openalex created_date 2022/08/01 · openalex updated_date 2026/07/28
Three-dimensional (3D) biomedical image sets are often acquired with in-plane\npixel spacings that are far less than the out-of-plane spacings between images.\nThe resultant anisotropy, which can be detrimental in many applications, can be\ndecreased using image interpolation. Optical flow and/or other\nregistration-based interpolators have proven useful in such interpolation roles\nin the past. When acquired images are comprised of signals that describe the\nflow velocity of fluids, additional information is available to guide the\ninterpolation process. In this paper, we present an optical-flow based\nframework for image interpolation that also minimizes resultant divergence in\nthe interpolated data.\n