Topic
Bicubic interpolation
About: Bicubic interpolation is a research topic. Over the lifetime, 3348 publications have been published within this topic receiving 73126 citations.
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01 Sep 2016
TL;DR: A superresolution algorithm used to reconstruct iris images based on Eigen-transformation of local image patches that is superior to bilinear or bicubic interpolation, especially at lower resolutions, and the fusion of the two systems pushes the EER to below 5% for down-sampling factors.
Abstract: Current research in iris recognition is moving towards enabling more relaxed acquisition conditions. This has effects on the quality of acquired images, with low resolution being a predominant issue. Here, we evaluate a superresolution algorithm used to reconstruct iris images based on Eigen-transformation of local image patches. Each patch is reconstructed separately, allowing better quality of enhanced images by preserving local information. Contrast enhancement is used to improve the reconstruction quality, while matcher fusion has been adopted to improve iris recognition performance. We validate the system using a database of 1,872 near-infrared iris images. The presented approach is superior to bilinear or bicubic interpolation, especially at lower resolutions, and the fusion of the two systems pushes the EER to below 5% for down-sampling factors up to a image size of only 13×13.
11 citations
Patent•
01 Feb 2002
TL;DR: In this article, a hybrid interpolation method is proposed that employs more than one interpolation algorithm and chooses the most appropriate interpolation that provides high quality images with a minimum processing time.
Abstract: Hybrid interpolation methods are provided that employ more than one interpolation algorithm and choose the most appropriate interpolation algorithm that provides high quality images with a minimum processing time. Prediction means is first applied to predict which interpolation is most appropriate for a given pixel in terms of complexity and performance. Then, a simple interpolation algorithm is used for pixels for which the simple interpolation algorithm provides acceptable performances and a complex interpolation algorithm is used for pixels for which the complex interpolation algorithm, which requires a larger number of operations, significantly outperforms the simple interpolation algorithm. Consequently, it is possible to obtain high quality images without significantly increasing the processing time.
11 citations
01 Feb 2016
TL;DR: This paper proposed a more systematic approach which has a parallel component known as skeleton which is implemented in various parallel paradigms like OpenMP, MPI, and CUDA etc and shows better performance with respect to other parallel methods.
Abstract: The cubic spline interpolation is frequently used for analysis the data set in various aspects of engineering and science problem. For a large set of data points defined with very large range, it is very difficult to interpolate by using traditional sequential algorithm. In this paper, we proposed a more systematic approach which has a parallel component known as skeleton which is implemented in various parallel paradigms like OpenMP, MPI, and CUDA etc. It is interesting that the skeleton approach is used with pipelining technique that gives better result as compared to the previous studies. This approach is applied to compute the cubic spline interpolating polynomial based on a large data set. The experimental result using the parallel skeleton technique on multi-core CPU and GPU shows better performance with respect to other parallel methods.
11 citations
TL;DR: In this paper, necessary and sufficient conditions for the convergence of cardinal interpolation with bivariate box splines as the degree tends to infinity are given. But they do not consider the case where the spline is fixed.
Abstract: We give necessary and sufficient conditions for the convergence of cardinal interpolation with bivariate box splines as the degree tends to infinity.
11 citations
01 Jan 2011
TL;DR: It is demonstrated that cross sections can be interpolated with the necessary accuracy on a sparse grid, which requires a significantly smaller number of sample points than the corresponding tensor product (full) grid.
Abstract: The problem considered in this paper involves the representation of few group, homogenized neutron cross sections. The method for cross section representation proposed and studied in this paper utilizes a hierarchical multilinear interpolation based on sparse grid nodes. Besides interpolation itself, the method includes a built-in means of estimating the interpolation error and a procedure for optimizing the representation with the goal to reduce its footprint and the cross section reconstruction time. The method was tested on the capture cross sections of a standard Material Test Reactor fuel element for different isotopes and the results were compared to a multilinear interpolation on a tensor product grid. It is demonstrated that cross sections can be interpolated with the necessary accuracy on a sparse grid, which requires a significantly smaller number of sample points than the corresponding tensor product (full) grid. The built-in means of estimating the interpolation error is shown to be conservative by comparison with the error estimated on independent samples. The representation optimization procedure allows the discarding of most of the terms in the tensor product interpolation, as well as many terms in the sparse grid interpolation with a minimal impact on the interpolation error.
11 citations