Bicubic interpolation

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

A new single image interpolation technique for super resolution

Abstract: Interpolation is a technique for obtaining new unknown data points within the range of discrete known data points In this paper we propose a new single image interpolation technique for Super resolution and demonstrate its effectiveness for aerial images The proposed approach is a fast hybrid method of switching between covariance based interpolation technique and curvature based interpolation technique The two interpolation techniques are applied on the basis of edges and smooth areas The proposed algorithm has shown encouraging results in terms of visual quality and processing time

Deep Generative Adversarial Networks for Thin-Section Infant MR Image Reconstruction

Abstract: Due to their high spatial resolution, thin-section magnetic resonance (MR) images serve as ideal medical images for brain structure investigation and brain surgery navigation. However, compared with the clinically widely used thick-section MR images, thin-section MR images are less available due to the imaging cost. Thin-section MR images of infants are even scarcer but are quite valuable for the study of human brain development. Therefore, we propose a method for the reconstruction of thin-section MR images from thick-section images. A two-stage reconstruction framework based on generative adversarial networks (GANs) and a convolutional neural network (CNN) is proposed to reconstruct thin-section MR images from thick-section images in the axial and sagittal planes. A 3D-Y-Net-GAN is first proposed to fuse MR images from the axial and sagittal planes and to achieve the first-stage thin-section reconstruction. A 3D-DenseU-Net followed by a stack of enhanced residual blocks is then proposed to provide further detail recalibrations and structural corrections in the sagittal plane. In this method, a comprehensive loss function is also proposed to help the networks capture more structural details. The reconstruction performance of the proposed method is compared with bicubic interpolation, sparse representation, and 3D-SRU-Net. Cross-validation based on 35 cases and independent testing based on two datasets with totally 114 cases reveal that, compared with the other three methods, the proposed method provides an average 23.5% improvement in peak signal-to-noise ratio (PSNR), 90.5% improvement in structural similarity (SSIM), and 21.5% improvement in normalized mutual information (NMI). The quantitative evaluation and visual inspection demonstrate that our proposed method outperforms those methods by reconstructing more realistic results with better structural details.

Interpolation in computing forward projections in direct three-dimensional reconstruction.

Abstract: Describes a simple formulation for computing forward projections directly from 3D data that is independent of any special geometries. Two-dimensional interpolation functions are used to weight the contribution of voxels in the 3D reconstruction to pixels in the 2D projection. The authors present a modified bicubic spline interpolation function, which gives better forward projections at all angles than the simpler nearest neighbour and bilinear interpolation schemes. In the authors' application, electron microscopy, a 3D image of a macromolecule or macromolecular complex can be reconstructed from a set of 2D projected images of different, individual macromolecules lying in different orientations with respect to one another. In particular, the 3D orientation of an individual nucleoprotein complex with respect to a set of reference axes can be determined by selective imaging of the nucleic acid component.

Cubic Spline Interpolation on Nested Polygon Triangulations

Abstract: : We develop an algorithm for constructing Lagrange and Hermite interpolation sets for spaces of cubic C(sup 1)-splines on general classes of triangulations built up of nested polygons whose vertices are connected by line segments. Additional assumptions on the triangulation are significantly reduced compared to the special class given in. Simultaneously, we have to determine the dimension of these spaces, which is not known in general. We also discuss the numerical aspects of the method.