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Cubic convolution interpolation for digital image processing
Abstract
Cubic convolution interpolation is a new technique for resampling discrete data. It has a number of desirable features which make it useful for image processing. The technique can be performed efficiently on a digital computer. The cubic convolution interpolation function converges uniformly to the function being interpolated as the sampling increment approaches zero. With the appropriate boundary conditions and constraints on the interpolation kernel, it can be shown that the order of accuracy of the cubic convolution method is between that of linear interpolation and that of cubic splines. A one-dimensional interpolation function is derived in this paper. A separable extension of this algorithm to two dimensions is applied to image data.
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“In this context, it is important to recognize that best results may be obtained from high-resolution images only when an appropriate classification model and interpolation methods are used.49”
From SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning (2025) · cites this paper as [49]
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Metadata
- Authors
- Keys, R.
- Venue
- IEEE Transactions on Acoustics, Speech, and Signal Processing
- Published
- 1981-12
- Volume
- 29
- Issue
- 6
- Pages
- 1153-1160
- ISSN
- 0096-3518
- Cited by
- 3,255
- References
- 0
- Language
- en
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