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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TL;DR: A method for preserving the contours or edges based on adaptive osculatory rational interpolation kernel function, which is built up by approximating the ideal interpolating kernel function by continued fractions is presented.
29 citations
TL;DR: The unified scheme allows piece-wise smooth, continuous and boundary preservation interpolation of DTI data, so that smooth fiber tracts can be tracked in a continuous manner and confined within the boundaries of the targeted structure.
29 citations
01 Nov 2012
TL;DR: Experimental results reveal that the proposed algorithm offers significantly higher-quality super-resolution than bicubic interpolation without the cost of training on an extensive training set of imagery as is typical of competing single-image techniques.
Abstract: Single-image super-resolution driven by multihypothesis prediction is considered The proposed strategy exploits self-similarities existing between image patches within a single image Specifically, each patch of a low-resolution image is represented as a linear combination of spatially surrounding hypothesis patches The coefficients of this representation are calculated using Tikhonov regularization and then used to generate a high-resolution image Experimental results reveal that the proposed algorithm offers significantly higher-quality super-resolution than bicubic interpolation without the cost of training on an extensive training set of imagery as is typical of competing single-image techniques
29 citations
TL;DR: The authors propose a nonlinear-filter-based approach to gray-scale interpolation of 3-D images, referred to as column-fitting interpolation, which is reminiscent of the maximum-homogeneity filter used for image enhancement.
Abstract: Three-dimensional (3-D) images are now common in radiology. A 3-D image is formed by stacking a contiguous sequence of two-dimensional cross-sectional images, or slices. Typically, the spacing between known slices is greater than the spacing between known points on a slice. Many visualization and image-analysis tasks, however, require the 3-D image to have equal sample spacing in all directions. To meet this requirement, one applies an interpolation technique to the known 3-D image to generate a new uniformly sampled 3-D image. The authors propose a nonlinear-filter-based approach to gray-scale interpolation of 3-D images. The method, referred to as column-fitting interpolation, is reminiscent of the maximum-homogeneity filter used for image enhancement. The authors also draw upon the paradigm of relaxation labeling to devise an improved column-fitting interpolator. Both methods are typically more effective than traditional gray-scale interpolation techniques.
29 citations
TL;DR: A new technique, which improves the performance of the traditional image magnification methods, is presented and results show that images generated by the current method have sharper edges as well as lower reconstruction mean-square errors than those produced by traditional methods.
29 citations