3D image interpolation based on directional coherence

Wang, Yongmei; Zhang, Zhunping; Guo, Baining

doi:10.1109/mmbia.2001.991734

Cited by 7 publications

(1 citation statement)

References 18 publications

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“…Here, it is important to remember that image interpolation algorithms belonging to the linear or non-adaptive category remain the most efficient among other interpolation algorithms. It is also important to remember that methods presented in this paper can be extended to 3D image interpolation operations to solve the specific problem of interest (in generating 3D models) by using them in simultaneous multidimensional interpolation operations [56,57].…”

Section: Introductionmentioning

confidence: 99%

Normalized Weighting Schemes for Image Interpolation Algorithms

Rukundo

2023

Applied Sciences

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Image interpolation algorithms pervade many modern image processing and analysis applications. However, when their weighting schemes inefficiently generate very unrealistic estimates, they may negatively affect the performance of the end-user applications. Therefore, in this work, the author introduced four weighting schemes based on some geometric shapes for digital image interpolation operations. Moreover, the quantity used to express the extent of each shape’s weight was the normalized area, especially when the sums of areas exceeded a unit square size. The introduced four weighting schemes are based on the minimum side-based diameter (MD) of a regular tetragon, hypotenuse-based radius (HR), the virtual pixel length-based height for the area of the triangle (AT), and the virtual pixel length for hypotenuse-based radius for the area of the circle (AC). At the smaller scaling ratio, the image interpolation algorithm based on the HR scheme scored the highest at 66.6% among non-traditional image interpolation algorithms presented. However, at the higher scaling ratio, the AC scheme-based image interpolation algorithm scored the highest at 66.6% among non-traditional algorithms presented, and, here, its image interpolation quality was generally superior or comparable to the quality of images interpolated by both non-traditional and traditional algorithms.

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Section: Introductionmentioning

confidence: 99%

Normalized Weighting Schemes for Image Interpolation Algorithms

Rukundo

2023

Applied Sciences

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Local up-sampling and morphological analysis of low-resolution magnetic resonance images

2017

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Data-adapted moving least squares method for 3-D image interpolation

Jang¹,

Nam²,

Lee³

et al. 2013

Phys. Med. Biol.

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In this paper, we present a nonlinear three-dimensional interpolation scheme for gray-level medical images. The scheme is based on the moving least squares method but introduces a fundamental modification. For a given evaluation point, the proposed method finds the local best approximation by reproducing polynomials of a certain degree. In particular, in order to obtain a better match to the local structures of the given image, we employ locally data-adapted least squares methods that can improve the classical one. Some numerical experiments are presented to demonstrate the performance of the proposed method. Five types of data sets are used: MR brain, MR foot, MR abdomen, CT head, and CT foot. From each of the five types, we choose five volumes. The scheme is compared with some well-known linear methods and other recently developed nonlinear methods. For quantitative comparison, we follow the paradigm proposed by Grevera and Udupa (1998). (Each slice is first assumed to be unknown then interpolated by each method. The performance of each interpolation method is assessed statistically.) The PSNR results for the estimated volumes are also provided. We observe that the new method generates better results in both quantitative and visual quality comparisons.

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3D image interpolation based on directional coherence

Cited by 7 publications

References 18 publications

Normalized Weighting Schemes for Image Interpolation Algorithms

Normalized Weighting Schemes for Image Interpolation Algorithms

Local up-sampling and morphological analysis of low-resolution magnetic resonance images

Data-adapted moving least squares method for 3-D image interpolation

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