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Bicubic interpolation

About: Bicubic interpolation is a research topic. Over the lifetime, 3348 publications have been published within this topic receiving 73126 citations.


Papers
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Proceedings ArticleDOI
11 Jul 2011
TL;DR: The proposed algorithm is based on examining the normalized energy density present within windows of varying size in the second derivative of the frequency domain, and exploiting this characteristic to derive a 19-dimensional feature vector that is used to train a SVM classifier.
Abstract: We propose a new method to detect re-sampled imagery. The method is based on examining the normalized energy density present within windows of varying size in the second derivative of the frequency domain, and exploiting this characteristic to derive a 19-dimensional feature vector that is used to train a SVM classifier. Experimental results are reported on 7,500 raw images from the BOSS database. Comparison with prior work reveals that the proposed algorithm performs similarly for re-sampling rates greater than 1, and is superior to prior work for re-sampling rates less than 1. Experiments are performed for both bilinear and bicubic interpolation, and qualitatively similar results are observed for each. Results are also provided for the detection of re-sampled imagery that subsequently undergoes JPEG compression. Results are quantitatively similar with some small degradation in performance as the quality factor is reduced.

23 citations

Patent
Georg Dipl.-Ing. Höhn1
21 Jan 1992
TL;DR: In this paper, the movement of a machine element is guided by a multi-axial, numerically controlled machine through the selection of interpolation points which are calculated and stored off-line, and a tool-radius correction is rendered possible.
Abstract: When the movement of a machine element is guided by a multi-axial, numerically controlled machine through the selection of interpolation points which are calculated and stored off-line, a tool-radius correction is rendered possible. This occurs because correction interpolation points are calculated on the basis of interpolation points and thus, with the least possible expenditure of time, a new trajectory curve can be generated, with which the radius changes are considered. In addition, a change in feedrate can be achieved through the selection of override values by placing fine interpolation points between two interpolation points at a time.

23 citations

Journal ArticleDOI
TL;DR: In this article, the Lagrange form is obtained for this interpolating polynomial and an algorithm is derived for evaluating it efficiently, which is a Lagrange-based algorithm for two-dimensional interpolation at points of a geometric mesh on a triangle.
Abstract: In an earlier paper [8], I. J. Schoenberg discussed polynomial interpolation in one dimension at the points of a geometric progression, which was originally proposed by James Stirling. In the present paper, these ideas are generalised to two-dimensional polynomial interpolation at the points of a geometric mesh on a triangle. A Lagrange form is obtained for this interpolating polynomial and an algorithm is derived for evaluating it efficiently.

23 citations

Journal ArticleDOI
TL;DR: An algorithm for high dynamic range (HDR) and super-resolution (SR) imaging from a single image based on the Retinex approach and a recent single image SR method based on a convolutional neural network (CNN).
Abstract: This paper presents an algorithm for high dynamic range (HDR) and super-resolution (SR) imaging from a single image. First, we propose a new single image HDR imaging (HDRI) method based on the Retinex approach and exploit a recent single image SR method based on a convolutional neural network (CNN). Among many possible configurations of HDR and SR, we find an optimal system configuration and color manipulation strategy from the extensive experiments. Specifically, the best results are obtained when we first process the luminance component ( $Y$ ) of input with our single image HDRI algorithm and then feed the enhanced HDR luminance to the CNN-based SR architecture that is trained by only luminance component. The ranges of chromatic components ( $U$ and $V$ ) are just scaled in proportion to the enhanced HDR luminance, and then they are bicubic interpolated or fed to the above CNN-based SR. Subjective and objective assessments for various experiments are presented to validate the effectiveness of the proposed HDR/SR imaging scheme.

23 citations

Proceedings ArticleDOI
24 Oct 1999
TL;DR: This paper shows that the concept of cubic convolution can be generalized, and derives kernels of up to ninth order and compares them both mutually and to cardinal splines of corresponding orders, which concludes that in all cases, Cardinal splines are superior.
Abstract: A well-known approach to image interpolation is cubic convolution, in which the ideal sine function is modelled by a finite extent kernel, which consists of piecewise third order polynomials In this paper we show that the concept of cubic convolution can be generalized We derive kernels of up to ninth order and compare them both mutually and to cardinal splines of corresponding orders From spectral analyses we conclude that the improvements of the higher order schemes over cubic convolution are only marginal We also conclude that in all cases, cardinal splines are superior

23 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202350
2022118
202187
202087
2019122
201892