Showing papers on "Bicubic interpolation published in 2009"

Directional View Interpolation for Compensation of Sparse Angular Sampling in Cone-Beam CT

Abstract: In flat detector cone-beam computed tomography and related applications, sparse angular sampling frequently leads to characteristic streak artifacts. To overcome this problem, it has been suggested to generate additional views by means of interpolation. The practicality of this approach is investigated in combination with a dedicated method for angular interpolation of 3-D sinogram data. For this purpose, a novel dedicated shape-driven directional interpolation algorithm based on a structure tensor approach is developed. Quantitative evaluation shows that this method clearly outperforms conventional scene-based interpolation schemes. Furthermore, the image quality trade-offs associated with the use of interpolated intermediate views are systematically evaluated for simulated and clinical cone-beam computed tomography data sets of the human head. It is found that utilization of directionally interpolated views significantly reduces streak artifacts and noise, at the expense of small introduced image blur.

Bidimensional empirical mode decomposition using various interpolation techniques

Abstract: Scattered data interpolation is an essential part of bidimensional empirical mode decomposition (BEMD) of an image. In the decomposition process, local maxima and minima of the image are extracted at each iteration and then interpolated to form the upper and the lower envelopes, respectively. The number of two-dimensional intrinsic mode functions resulting from the decomposition and their properties are highly dependent on the method of interpolation. Though a few methods of interpolation have been tested and/or applied to the BEMD process, many others remain to be tested. This paper evaluates the performance of some of the widely used surface interpolation techniques to identify one or more good choices of such methods for envelope estimation in BEMD. The interpolation techniques studied in this paper include various radial basis function interpolators and Delaunay triangulation based interpolators. The analysis is done first using a synthetic texture image and then using two different real texture images. Simulations are made to focus mainly on the effect of interpolation methods by providing less or negligible control on the other parameters or factors of the BEMD process.

Image Scaling Comparison Using Universal Image Quality Index

Abstract: Image interpolation has many applications in computer vision, image processing and biomedical applications. Resampling is required for discrete image manipulations, such as geometric alignment and registration, to improve image quality on display devices or in the field of lossy image compression wherein some pixels are discarded during the encoding process and must be regenerated from the remaining information for decoding. The comparison is done for different interpolation techniques such as nearest neighbor, bilinear and bicubic interpolation and the comparison is done for different interpolation schemes using universal image quality index. In this paper an attempt is made to highlight the universal quality index by comparing with error measures such as MSE and PSNR.

Nonlocal Edge-Directed Interpolation

Abstract: In this paper, we proposed a new edge-directed image interpolation algorithm which can preserve the edge features and natural appearance of images efficiently. In the proposed scheme, we first get a close-form solution of the optimal interpolation coefficients under the sense of minimal mean square error by exploiting autoregressive model (AR) and the geometric duality between the low-resolution and high-resolution images .Then the coefficients of the Nonlocal Edge-directed interpolation (NLEDI) are derived with structure similarity in images, which are solutions of weighted least square equations. The new image interpolation approach preserves spatial coherence of the interpolated images better than the existing methods and it outperforms the other methods in terms of objective and subjective image quality.

Advanced Bilinear Image Interpolation Based on Edge Features

Abstract: A variety of image interpolation methods have been used in order to obtain high-resolution (HR) images. In this paper, we propose an advanced bilinear interpolation algorithm which improves edge components. The conventional bilinear image interpolation method has a serious problem such as blurring artifacts. In the experimental results, our proposed algorithm outperforms the bilinear image interpolation not only objective qualities but also subjective qualities.

High-Rate Interpolation of Random Signals From Nonideal Samples

Abstract: We address the problem of reconstructing a random signal from samples of its filtered version using a given interpolation kernel. In order to reduce the mean squared error (MSE) when using a nonoptimal kernel, we propose a high rate interpolation scheme in which the interpolation grid is finer than the sampling grid. A digital correction system that processes the samples prior to their multiplication with the shifts of the interpolation kernel is developed. This system is constructed such that the reconstructed signal is the linear minimum MSE (LMMSE) estimate of the original signal given its samples. An analytic expression for the MSE as a function of the interpolation rate is provided, which leads to an explicit condition such that the optimal MSE is achieved with the given nonoptimal kernel. Simulations confirm the reduction in MSE with respect to a system with equal sampling and reconstruction rates.

Multiresolution Mean Shift Clustering Algorithm for Shape Interpolation

Abstract: In this paper, we solve the problem of 3D shape interpolation with significant pose variation. For an ideal 3D shape interpolation, especially the articulated model, the shape should follow the movement of the underlying articulated structure and be transformed in a way that is as rigid as possible. Given input shapes with compatible connectivity, we propose a novel multiresolution mean shift (MMS) clustering algorithm to automatically extract their near-rigid components. Then, by building the hierarchical relationship among extracted components, we compute a common articulated structure for these input shapes. With the aid of this articulated structure, we solve the shape interpolation by combining 1) a global pose interpolation of near-rigid components from the source shape to the target shape with 2) a local gradient field interpolation for each pair of components, followed by solving a Poisson equation in order to reconstruct an interpolated shape. As a result, an aesthetically pleasing shape interpolation can be generated, with even the poses of shapes varying significantly. In contrast to a recent state-of-the-art work (Kilian et al., 2007), the proposed approach can achieve comparable or even better results and have better computational efficiency as well.

Performance analysis of accelerated image registration using GPGPU

Abstract: This paper presents a performance analysis of an accelerated 2-D rigid image registration implementation that employs the Compute Unified Device Architecture (CUDA) programming environment to take advantage of the parallel processing capabilities of NVIDIA's Tesla C870 GPU. We explain the underlying structure of the GPU implementation and compare its performance and accuracy against a fast CPU-based implementation. Our experimental results demonstrate that our GPU version is capable of up to 90x speedup with bilinear interpolation and 30x speedup with bicubic interpolation while maintaining a high level of accuracy. This compares favorably to recent image registration studies, but it also indicates that our implementation only reaches about 70% of theorectical peak performance. To analyze our results, we utilize profiling data to identify some of the underlying limitations of CUDA that prohibit peak performance. At the end, we emphasize the need to manage memory resources carefully to fully utilize the GPU and obtain maximum speedup.

Geometric Hermite interpolation by cubic G1 splines

Abstract: In this paper, geometric Hermite interpolation by planar cubic G 1 splines is studied. Three data points and three tangent directions are interpolated per polynomial segment. Sufficient conditions for the existence of such a G 1 spline are determined that cover most of the cases encountered in practical applications. The existence requirements are based only upon geometric properties of data and can easily be verified in advance. The optimal approximation order 6 is confirmed, too.

On the Role of Exponential Splines in Image Interpolation

Abstract: A Sobolev reproducing-kernel Hilbert space approach to image interpolation is introduced. The underlying kernels are exponential functions and are related to stochastic autoregressive image modeling. The corresponding image interpolants can be implemented effectively using compactly-supported exponential B-splines. A tight l2 upper-bound on the interpolation error is then derived, suggesting that the proposed exponential functions are optimal in this regard. Experimental results indicate that the proposed interpolation approach with properly-tuned, signal-dependent weights outperforms currently available polynomial B-spline models of comparable order. Furthermore, a unified approach to image interpolation by ideal and nonideal sampling procedures is derived, suggesting that the proposed exponential kernels may have a significant role in image modeling as well. Our conclusion is that the proposed Sobolev-based approach could be instrumental and a preferred alternative in many interpolation tasks.

Design of piecewise weighted linear interpolation based on even-odd decomposition and its application to image resizing

Abstract: In this paper, an image interpolation method based on even-odd decomposition (EOD) is proposed. An input signal for interpolation is decomposed to even and odd vectors by EOD. And then different interpolation methods are applied to even and odd vectors, respectively. This paper presents an analysis on the two vectors and new design methods for them. Also, based on the new design method, a signal flow graph of the proposed method is provided and compared with the CCI method in terms of complexity. To evaluate the proposed method, we conduct experiments and complexity comparison. The results indicate that the proposed interpolation method does not only outperform the existing method in terms of objective and subjective image quality but also requires less complexity.

Reversible Interpolation of Vectorial Images by an Anisotropic Diffusion-Projection PDE

Abstract: In this paper, a nonlinear model for the interpolation of vector-valued images is proposed. This model is based on an anisotropic diffusion PDE and performs an interpolation that is reversible. The interpolation solution is restricted to the subspace of functions that can recover the discrete input image, after an appropriate smoothing and sampling. The proposed nonlinear diffusion flow lies on this subspace while its strength and anisotropy adapt to the local variations and geometry of image structures. The derived method effectively reconstructs the real image structures and yields a satisfactory interpolation result. Compared to classic and other existing PDE-based interpolation methods, our proposed method seems to increase the accuracy of the result and to reduce the undesirable artifacts, such as blurring, ringing, block effects and edge distortion. We present extensive experimental results that demonstrate the potential of the method as applied to graylevel and color images.

Interpolation on a Magnetic Field.

Abstract: As neutrons pass through a magnetic field, their spin precesses. We seek to simulate this precession within the neutron tracing package McStas. Presented here are the methods and implementation details used to get arbitrary magnetic field data values when only a finite number of points are known.

A sub-pixel stereo matching algorithm and its applications in fabric imaging

Abstract: In this paper, we describe a sub-pixel stereo matching algorithm where disparities are iteratively refined within a regularization framework We choose normalized cross-correlation as the matching metric, and perform disparity refinement based on correlation gradients, which is distinguished from intensity gradient-based methods We propose a discontinuity-preserving regularization technique which utilizes local coherence in the disparity space image, instead of estimating discontinuities in the intensity images A concise numerical solution is derived by parameterizing the disparity space with dense bicubic B-splines Experimental results show that the proposed algorithm performs better than correlation fitting methods without regularization The algorithm has been implemented for applications in fabric imaging We have shown its potentials in wrinkle evaluation, drape measurement, and pilling assessment

Super-Resolution Image with Estimated High Frequency Compensated algorithm

Abstract: In this paper, we propose an Estimated High Frequency Compensated (EHFC) algorithm for super resolution images. It is based on Iterative Back Projection (IBP) method combined with compensated high frequency models according to different applications. The proposed algorithm not only improves the quality of enlarged images produced by zero-order, bilinear, or bicubic interpolation methods, but also accelerates the convergence speed of IBP. In experiments with general tested images, EHFC method can increase the speed by 1 ∼ 6.5 times and gets 0.4 ∼ 0.7 dB PSNR gain. In text image tests, EHFC method can increase 1.5 ∼ 6.5 times in speed and 1.2 ∼ 8.3 dB improvement in PSNR.

A note on stability results for scattered data interpolation on Euclidean spheres

Abstract: The present work considers the interpolation of the scattered data on the d-sphere by spherical polynomials. We prove bounds on the conditioning of the problem which rely only on the separation distance of the sampling nodes and on the degree of polynomials being used. To this end, we establish a packing argument for well separated sampling nodes and construct strongly localized polynomials on spheres. Numerical results illustrate our theoretical findings.

Improvement of image zooming using least directional differences based on linear and cubic interpolation

Abstract: There are many interpolation methods, among them, bilinear (BL) and bicubic (BC) are more popular. However, these methods suffer from low quality edge blurring and aliasing effect. In the other hand, if high resolution images are not available, it is impossible to produce high quality display images and prints. To overcome this drawback, in this paper, we proposed a new method that uses least directional differences of neighbor pixels, based on preceding bilinear and bicubic interpolation methods for images. The qualitative and quantitative results of proposed technique show that this method improves bilinear and bicubic interpolations. The proposed algorithm can also be applied both to RGB and gray level images.

Fast directional image interpolation with difference projection

Abstract: This paper presents a new directional image interpolator, aiming to increase image resolution with high perceptual quality and low computational complexity. In our method, missing pixels in a magnified image are generated through linear interpolation on certain fixed supports to facilitate fast implementation, while local directional features are imposed on the adaptive interpolation weights which are determined by the gradients diffused from the low resolution image. Afterwards, a novel difference projection strategy is proposed to enforce the continuity of the magnified image by reusing the directional interpolator. Experimental results show that our method outperforms conventional bicubic and some existing adaptive interpolators, in terms of both the perceptual and quantitative quality.

Application of generalized regression neural network residual kriging for terrain surface interpolation

Abstract: Spatial interpolation techniques are a powerful tool for generating visually continuous surfaces from scattered point data, and the accuracy of interpolation determines the practical values of interpolating surfaces As the variation of surface elevation is nonlinear, the conventional spatial interpolation models implemented in many GIS packages sometime cannot provide appreciate interpolation accuracy for certain application due to their nature of linear estimation In this paper, a method of generalized regression neural network residual kriging (GRNNRK) was presented for terrain surface interpolation The GRNNRK was a two-step algorithm The first step included estimating the overall nonlinear spatial structures by generalized regression neural network (GRNN), and the second step was the analysis of the stationary residuals by ordinary kriging And the final estimates were produced as a sum of GRNN estimates and ordinary kriging estimates of residuals To test performance of GRNNRK, a total of 1089 scattered elevation data got from 2886 km 2 area were split into independent training data set (200) and validation data set (889), and the training data set was modeled for terrain surface interpolation using ordinary kriging and GRNNRK, respectively, while the validation data set was used to test their accuracies The results showed that GRNNRK could achieve better accuracy than kriging for interpolating surfaces Therefore, GRNNRK was an efficient alternative to the conventional spatial interpolation models usually used for scattered data interpolation in terrain surface interpolation

Application of bicubic splines in the boundary element method

Abstract: In this paper, we proposed the method for improving the accuracy of BEM, which is based on application of bicubic splines for interpolation functions. Application of bicubic splines ensures continuity of class C 1 at the boundaries of element. Such an interpolation results in smooth approximation of the surface sources leading to high accuracy of computation. Set of integral equations is solved by implementation of Galerkin method for determination of unknown coefficients.The accuracy of the proposed approach is illustrated by comparison of the solution of electric field in thin-plate capacitor by BEM using bicubic splines, second-order polynomial, linear and piecewise-constant interpolation.

Methods of Increasing Spatial Resolution of Digital Images with Minimum Detail Loss and its Applications

Abstract: This paper discusses the techniques that can increase the resolution of a digital image and its applications. The current methods used for increasing the resolution are the interpolation methods, which are Bicubic that is the slowest but produces the best estimation of new pixel values. Bilinear is faster than Bicubic, but does a weaker job. Both Bicubic and Bilinear interpolation result in a blurred image, especially when upsampling. The next method is the Nearest Neighbor, does not use interpolation but simply takes the value of the neighboring pixels and adds new pixels without averaging them, which gives jaggies or stair-case effect. These interpolation methods have downfalls, as it results in blurring of the resized image, especially in areas with sharp lines and distinct changes in color. There are three new methods of increasing image resolution proposed in the paper. The first method will work only on bicolour images like the medical scan images. The raster image of the scan is converted to a vector image where the pixels of the image are converted to geometrical functions and algorithms. The next method involves projecting the image on a plane and then captured by a camera sensor. The resultant image produced by the sensor will be much bigger than input image. The applications of increasing image resolution can provide better detailed zooming and magnification of details in an image, conversion of low-resolution digital images and videos to a higher resolution. The concept of increasing and decreasing the resolution of an image can be used, instead of compression, for sending images over a network.

Loop subdivision surfaces interpolating B-spline curves

Abstract: This paper presents a novel method for defining a Loop subdivision surface interpolating a set of popularly-used cubic B-spline curves. Although any curve on a Loop surface corresponding to a regular edge path is usually a piecewise quartic polynomial curve, it is found that the curve can be reduced to a single cubic B-spline curve under certain constraints of the local control vertices. Given a set of cubic B-spline curves, it is therefore possible to define a Loop surface interpolating the input curves by enforcing the interpolation constraints. In order to produce a surface of local or global fair effect, an energy-based optimization scheme is used to update the control vertices of the Loop surface subjecting to curve interpolation constraints, and the resulting surface will exactly interpolate the given curves. In addition to curve interpolation, other linear constraints can also be conveniently incorporated. Because both Loop subdivision surfaces and cubic B-spline curves are popularly used in engineering applications, the curve interpolation method proposed in this paper offers an attractive and essential modeling tool for computer-aided design.

Kind of super-resolution method of CCD image based on wavelet and bicubic interpolation

Abstract: In order to maintain the original information of the CCD images,to improve the spatial resolution of image,to observe and analyze the details of the images,this paper studied on various super-resolution methods.This paper proposed the improved super-resolution method based on wavelets and bicubic interpolation.It transformed low-resolution images of gray-scale,and considered it as the low-resolution image in an inverse wavelet transform,recoveried the super-resolution image,then got an average of with the image of low-resolution's bicubic interpolation.Applied this method in CCD images,improved the spatial resolution in the visual,and made the peak signal to noise ratio 25.524 4 dB.Experimental results show that using this method in low-resolution image can get higher PSNR and better image's details effect than those only using bicubic interpolation in the whole wavelet domain or those using the original image as the low-frequency image in an inverse wavelet transform of the bicubic interpolation or those only using the bilinear.The experiment results show that this method is an effective way for improving the CCD image's resolution.