Interpolation Revisited

By Philippe Thévenaz, Thierry Blu, Michael A. Unser. IEEE Trans. Medical Imaging, 2000

553 citations recorded by the source corpus.

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Citations in the corpus

Linear interpolation revitalized (2004, background): “We have shown in [4], [12] that both the interpolation error and the orthogonal projection error can be estimated very accurately by (5) where the function is what we call a Fourier kernel of approximation.”
Point spread function mapping with parallel imaging techniques and high acceleration factors: Fast, robust, and flexible method for echo‐planar imaging distortion correction (2004, background): “Maximal-order interpolation methods of minimal support appear to fulfill both requirements of interpolation accuracy and acceptable computation times (18,19).”
A Review on Medical Image Registration as an Optimization Problem (2017, methodology): “Similar to TPS, B-splines also optimize the projective correlation between images by solving a series of parameters [40].”
Ncorr: Open-Source 2D Digital Image Correlation Matlab Software (2015, background): “D convolutions (since biquintic B-spline interpolation is separable [49]), which reduces to a series of simple dot products.”
Within-subject template estimation for unbiased longitudinal image analysis (2012, methodology): “For this paper, we employ linear interpolation, but recently switched to cubic B-spline interpolation for future releases to reduce interpolation artifacts (Thevenaz et al., 2000).”
Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images (2004, methodology): “To obtain accurate reference tracings, required for the quantitative analyses described hereafter, high-resolution versions of the original images were generated by scaling up with a factor 10 using a high-quality interpolation method: cubic spline interpolation (24,25).”
Image registration methods: a survey (2003, background): “[183] have proposed a different approach to image resampling.”
A chronology of interpolation: from ancient astronomy to modern signal and image processing (2002, methodology): “The loss of information and the introduction of distortions and artifacts caused by any manipulation of image data should be minimized in order to minimize their influence on the clinicians’ judgements [276].”
AMIDE: a free software tool for multimodality medical image analysis. (2003, methodology): “The resolutions of the data sets were 1.5 mm and 0.4 mm for the PET and CT, respectively.”
Digital Topology and Geometry in Medical Imaging: A Survey (2015, background): “Interpolation techniques may be classified into two categories, namely, image-based [56], [57] and object-based [7], [139]–[141].”
Medical image registration: a review (2014, background): “A study on image interpolation function can be found in The´venaz et al. (2000).”
A fast diffeomorphic image registration algorithm (2007, methodology): “It would be possible to use a higher-degree interpolation (see e.g. Thévenaz et al., 2000), but linear interpolation was used for speed.”
elastix: A Toolbox for Intensity-Based Medical Image Registration (2010, background): “polation exist, varying in quality and speed, including nearest neighbor, linear and th-order B-spline interpolation [53], [59].”
Time series interpolation via global optimization of moments fitting (2013, unknown): “[4,20,38,37], to sinc interpolation or wavelets (in connection to filtering and signal processing), see [35] and the references given there, and Chapter 19 in [34] for a detailed description of such methods.”
Elastic registration of biological images using vector-spline regularization (2005, background): “Cubic B-spline interpolation offers a good tradeoff between accuracy and speed [18], [19].”
User‐friendly semiautomated assembly of accurate image mosaics in microscopy (2007, methodology): “The motivations and benefits of the various ingredients can be found in the relevant references: cubic-spline interpolation model (Thévenaz et al., 2000; Unser, 1999), quasi-Newton optimization strategy (Thévenaz et al., 1998), multiresolution image representation (Unser et al., 1993), and maximum…”