Showing papers on "Bicubic interpolation published in 2011"

Bilinear and bicubic interpolation methods for division of focal plane polarimeters.

Abstract: This paper presents bilinear and bicubic interpolation methods tailored for the division of focal plane polarization imaging sensor. The interpolation methods are targeted for a 1-Mega pixel polarization imaging sensor operating in the visible spectrum. The five interpolation methods considered in this paper are: bilinear, weighted bilinear, bicubic spline, an approximated bicubic spline and a bicubic interpolation method. The modulation transfer function analysis is applied to the different interpolation methods, and test images as well as numerical error analyses are also presented. Based on the comparison results, the full frame bicubic spline interpolation achieves the best performance for polarization images.

Accuracy enhancement of digital image correlation with B-spline interpolation

Abstract: The interpolation algorithm plays an essential role in the digital image correlation (DIC) technique for shape, deformation, and motion measurements with subpixel accuracies. At the present, little effort has been made to improve the interpolation methods used in DIC. In this Letter, a family of recursive interpolation schemes based on B-spline representation and its inverse gradient weighting version is employed to enhance the accuracy of DIC analysis. Theories are introduced, and simulation results are presented to illustrate the effectiveness of the method as compared with the common bicubic interpolation.

Linear Methods for Image Interpolation

Abstract: We discuss linear methods for interpolation, including nearest neighbor, bilinear, bicubic, splines, and sinc interpolation. We focus on separable interpolation, so most of what is said applies to one-dimensional interpolation as well as N-dimensional separable interpolation. Source Code The source code (ANSI C), its documentation, and the online demo are accessible at the IPOL web page of this article 1 .

Value Function Iteration as a Solution Method for the Ramsey Model

Abstract: Summary Value function iteration is one of the standard tools for the solution of dynamic general equili brium models if the dimension of the state space is one ore two. We consider three kinds of models: the deterministic and the stochastic growth model and a simple heterogenous agent model. Each model is solved with six different algorithms: (1) simple value function iteration as compared to (2) smart value function iteration neglects the special structure of the problem. (3) Full and (4) modified policy iteration are methods to speed up convergence. (5) linear and (6) cubic interpolation between the grid points are methods that enhance precision and reduce the size of the grid. We evaluate the algorithms with respect to speed and accuracy. Accuracy is defined as the maximum absolute value of the residual of the Euler equation that determines the household's savings. We demonstrate that the run time of all algorithms can be reduced substantially if the value function is initialized stepwise, starting on a coarse grid and increasing the number of grid points successively until the desired size is reached. We find that value func tion iteration with cubic spline interpolation between grid points dominates the other methods if a high level of accuracy is needed.

Cubic spline interpolation on new condition

Abstract: Based on analysis of basic cubic spline interpolation,the traditional cubic spline interpolation is generalized.The methods are presented on the condition that the first derivative and second derivative of arbitrary node are given.At last,these calculation methods are illustrated through the examples.

Contour Stencils: Total Variation along Curves for Adaptive Image Interpolation

Abstract: Image interpolation is the problem of increasing the resolution of a given image. An important aspect of interpolation is accurate estimation of edge orientations. This work introduces contour stencils, a new method for estimating the image contours based on total variation along curves. Contour stencils are able to distinguish lines of different orientations, curves, corners, and other geometric features with a computationally efficient formula. Contour stencils are applied in designing an edge directed color interpolation method. The method incorporates an efficient approximation of deconvolution. Although most interpolation methods that involve deconvolution require either solving a large linear system or running many iterations, this method has linear complexity in the number of pixels and can be computed in one or a small number of passes through the image. Comparisons show that the proposed interpolation is competitive with existing methods.

Method and device for interpolating images by using a smoothing interpolation filter

Abstract: Provided are a method of interpolating an image by determining interpolation filter coefficients, and an apparatus for performing the same. The method includes: differently selecting an interpolation filter, from among interpolation filters for generating at least one sub-pel-unit pixel value located between integer-pel-unit pixels, based on a sub-pel-unit interpolation location and a smoothness; and generating the at least one sub-pel-unit pixel value by interpolating, using the selected interpolation filter, pixel values of the integer-pel-unit pixels.

Numerical method using cubic B-spline for the heat and wave equation

Abstract: In the present paper, the cubic B-splines method is considered for solving one-dimensional heat and wave equations. A typical finite difference approach had been used to discretize the time derivative while the cubic B-spline is applied as an interpolation function in the space dimension. The accuracy of the method for both equations is discussed. The efficiency of the method is illustrated by some test problems. The numerical results are found to be in good agreement with the exact solution.

Adaptive DE-based reversible steganographic technique using bilinear interpolation and simplified location map

Abstract: In this paper, an adaptive DE-based reversible steganographic scheme with bilinear interpolation and simplified location map is proposed. In traditional reversible difference expansion (DE) scheme, it suffers from two problems: the embeddable location is considered insufficient and the embedding payload control capability in single layer embedding is weak. For the first problem, the kernel of bilinear interpolation is applied to effectively improve the number of the embeddable location while the quality of the stego-image can be maintained at a good level. In addition, the proposed simplified location map is used for the existing adaptive embedding rule to improve the second problem where the secret data can be adaptively embedded and also the load of additional information can be reduced. The experimental results revealed that the proposed scheme presented better visual quality of the stego-image and carried larger embedding payload than some other revised DE schemes, such as Alattar's and Lee's schemes.

An energy-based method for the forensic detection of Re-sampled images

Abstract: We propose a new method to detect re-sampled imagery. The method is based on examining the normalized energy density present within windows of varying size in the second derivative of the frequency domain, and exploiting this characteristic to derive a 19-dimensional feature vector that is used to train a SVM classifier. Experimental results are reported on 7,500 raw images from the BOSS database. Comparison with prior work reveals that the proposed algorithm performs similarly for re-sampling rates greater than 1, and is superior to prior work for re-sampling rates less than 1. Experiments are performed for both bilinear and bicubic interpolation, and qualitatively similar results are observed for each. Results are also provided for the detection of re-sampled imagery that subsequently undergoes JPEG compression. Results are quantitatively similar with some small degradation in performance as the quality factor is reduced.

3d image interpolation device, 3d imaging apparatus, and 3d image interpolation method

Abstract: A 3D image interpolation device performs frame interpolation on 3D video. The 3D image interpolation device includes: a range image interpolation unit that generates at least one interpolation range image to be interpolated between a first range image indicating a depth of a first image included in the 3D video and a second range image indicating a depth of a second image included in the 3D video; an image interpolation unit that generates at least one interpolation image to be interpolated between the first image and the second image; and an interpolation parallax image generation unit generates, based on interpolation image, at least one pair of interpolation parallax images having parallax according to a depth indicated by the interpolation range image.

Similarity modulated block estimation for image interpolation

Abstract: Modeling the nonstationarity of image signals is one of the challenging issues for image interpolation In this paper, we propose a similarity probability modeling to faithfully characterize the nonstationarity of image signals, and present a novel image interpolation algorithm based on the proposed model The missing pixels are estimated in groups by weighted block estimation The weight of each pixel inside the block is defined as the similarity probability between itself and the centered to-be-interpolated pixel It is demonstrated by the experimental results that the proposed method preserves the edge structures of the interpolated images better than the state-of-the-art interpolation methods Annoying artifacts nearby the sharp edges are also greatly reduced

Bicubic B-spline blending patches with optimized shape

Abstract: Two constructions of bicubic B-spline patches with fixed boundary conditions are described. Their goal is to minimize functionals taken for measures of patch badness. The first construction is numerically solving the triharmonic equation -@D^3p=0. The functional minimized in the second construction is the sum of a term determined by the surface shape (the distribution of mean curvature) and a term introduced to overcome the problem of ambiguity of minimum of the first term. In addition to boundary conditions one can impose constraints, e.g. fix constant parameter curves of the patch.

Extended cubic B-spline method for linear two-point boundary value problems

Abstract: Second order linear two-point boundary value problems were solved using extended cubic B-spline interpolation method. Extended cubic B-spline is an extension of cubic B-spline consisting of one shape parameter, called λ. The resulting approximated analytical solution for the problems would be a function of λ. Optimization of λ was carried out to find the best value of λ that generates the closest fit to the differential equations in the problems. This method approximated the solutions for the problems much more accurately compared to finite difference, finite element, finite volume and cubic B-spline interpolation methods.

Learning context-aware sparse representation for single image super-resolution

Abstract: This paper presents a novel learning-based method for single image super-resolution (SR). Given an input low-resolution image and its image pyramid, we propose to perform context-constrained image segmentation and construct an image segment dataset with different context categories. By learning context-specific image sparse representation, our method aims to model the relationship between the interpolated image patches and their ground truth pixel values from different context categories via support vector regression (SVR). To synthesize the final SR output, we upsample the input image by bicubic interpolation, followed by the refinement of each image patch using the SVR model learned from the associated context category. Unlike prior learning-based SR methods, our approach does not require the reoccurrence of similar image patches (within or across image scales), and we do not need to collect training low and high-resolution image data in advance either. Empirical results show that our proposed method is quantitatively and qualitatively more effective than existing interpolation or learning-based SR approaches.

Image interpolation methods evaluation for division of focal plane polarimeters

Abstract: This paper presents different imaging interpolation methods implemented for the division of focal plane polarization imaging sensor. The targeted polarization imaging sensor is a CCD based sensor with 1-Mega pixels resolution operating from 400nm to 1050nm wavelength. The five interpolation methods considered in this paper are: bilinear, weighted bilinear, bicubic spline, an approximated bicubic spline and a bicubic interpolation method. Test images of the five different interpolation methods as well as numerical error analysis are presented. Based on the comparison results, the full frame bicubic spline interpolation achieves the best performance for polarization images.

A new adaptive interpolation scheme of NURBS based on axis dynamics

Abstract: A new interpolation scheme for 2D NURBS curve is proposed in this paper. The scheme contains two steps, pre-processing and real-time interpolation, which are bridged by circular buffer and table look-up method in order to improve the data utilization and simplify the calculation process. The pre-processing divides the curve into segments by dangerous points, then real-time interpolation is implemented within the segment unit. During this process, adaptive interpolation algorithm adjusts the feed rate according to the curvature which guarantees the contour accuracy; meanwhile, the axis dynamics module confines the dynamics parameters of every axis within limits, without requiring look-ahead and backtracking strategy. At the end, experiments verify the high efficiency and reliability of the algorithm.

Image Interpolation Using Autoregressive Model and Gauss-Seidel Optimization

Abstract: In this paper we propose a simple yet effective image interpolation algorithm based on autoregressive model. Unlike existing algorithms which rely on low resolution pixels to estimate interpolation coefficients, we optimize the interpolation coefficients and high resolution pixel values jointly from one optimization problem. Although the two sets of variables are coupled in the cost function, the problem can be effectively solved using Gauss-Seidel method. We prove the iterations are guaranteed to converge. Experiments show that on average we have over 3dB gain compared to bicubic interpolation and over 0.1dB gain compared to SAI.

Image interpolation based on the pixel value corresponding to the smallest absolute difference

Abstract: This paper proposes a novel algorithm for image interpolation. The motivation is based on relentless image interpolation artefacts and computational complexity. The solution proposed is founded on reprocessing one of the four pixels surrounding the unknown location and calculating the mean between that pixel and the value introduced by the bilinear interpolation. Subsequently, the mean is multiplied by the control factor k whose value is selected according to experimental analysis. Experimental results are further presented to show the effectiveness and performance of the proposed algorithm.

Image interpolation method and apparatus

Abstract: The present invention relates to an image interpolation method and apparatus. The image interpolation method according to the present invention involves selecting different interpolation filters depending on the location of a sub-pixel between integer pixels, and generates a sub-pixel value at the location of the sub-pixel using the selected interpolation filter.

Method and apparatus for motion-compensated interpolation (MCI) with conservative motion model

Abstract: Motion compensated interpolation (MCI) methods and apparatus are presented for constructing interpolation image frames by computing at least one interpolated motion field at a temporal interpolation time using a conservative motion equation system, and computing at least one interpolation image frame based on the computed interpolated motion field using an MCI equation system.

Projection onto convex sets super-resolution image reconstruction based on wavelet bi-cubic interpolation

Abstract: The traditional projections onto convex sets super-resolution image reconstruction algorithm leads to the halo effect in reconstructed high resolution image, so we present a super-resolution reconstruction method based on wavelet bicubic interpolation algorithm. The new algorithm utilizes wavelet bi-cubic interpolation algorithm to obtain the initial image of POCS algorithm and introduces bilateral filter to estimate point spread function. Bilateral filtering makes non-edge pixel having minimum influence on the edge region. Algorithm also uses adaptive relaxation parameters to reduce the impact of error motion information. Compared with traditional algorithm, experiment results show that the proposed reconstruction algorithm eliminates the halo effect obviously, thus the reconstructed image can achieve a good visual effect.

A Fast Morphing-Based Interpolation For Medical Images : Application To Conformal Radiotherapy

Abstract: A method is presented for fast interpolation between medical images. The method is intended for both slice and projective interpolation. It allows ofine interpolation between neighboring slices in tomographic data. Spatial correspondence between adjacent images is established using a block matching algorithm. Interpolation of image intensities is then carried out by morphing between the images. The morphing-based method is compared to standard linear interpolation, block-matching-based interpolation and registrationbased interpolation in 3D tomographic data sets. Results show that the proposed method scored similar performance in comparison to registration-based interpolation, and signicantly outperforms both linear and block-matching-based interpolation. This method is applied in the context of conformal radiotherapy for online projective interpolation between Digitally Reconstructed Radiographs (DRRs).

Hierarchical, multilinear representation of few-group cross sections on sparse grids

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.

Quasi Interpolation With Voronoi Splines

Abstract: We present a quasi interpolation framework that attains the optimal approximation-order of Voronoi splines for reconstruction of volumetric data sampled on general lattices. The quasi interpolation framework of Voronoi splines provides an unbiased reconstruction method across various lattices. Therefore this framework allows us to analyze and contrast the sampling-theoretic performance of general lattices, using signal reconstruction, in an unbiased manner. Our quasi interpolation methodology is implemented as an efficient FIR filter that can be applied online or as a preprocessing step. We present visual and numerical experiments that demonstrate the improved accuracy of reconstruction across lattices, using the quasi interpolation framework.

A Hybrid Image Compression Technique Using Wavelet Transformation - MFOCPN and Interpolation

Abstract: In this paper an interpolation method is proposed for compression technique. The method used is the localizing of spatial and frequency correlation from wavelets. Modified Forward Only Counter Propagation Neural Network (MFOCPN) is used for the classification and functional task. The wavelet based technique decomposes the lower sub band consisting of non significant coefficients and are eliminated. The significant smooth and sharp coefficients are found using interpolation methods. Here a new technique is proposed called the cosine interpolation, which is an alternative to the nearest neighborhood interpolation method. This methodology of interpolation proved to be an efficient approach for mapping all significant coefficients and thus resulting in improved quality. Hence the comparison is made between nearest neighborhood interpolation and cosine interpolation. The experimental results are tested on various standard images, where these results yield a better PSNR value compared with the existing nearest neighbor interpolation method.

Fast algorithm of modified cubic convolution interpolation

Abstract: Cubic convolution interpolation algorithm is one of the most widely used image interpolation methods. Compared with the linear interpolation, it has a more accurate result, but a lower interpolation speed due to its computational complexity. The cubic convolution algorithm needs sixteen points in two-dimensional image interpolation. The new algorithm proposed in this paper reduces the necessary points to nine by using the correlation among neighboring pixels. Furthermore, the times of multiplication and addition involved in interpolation can be reduced to 53% and 48% respectively of those by original cubic convolution. So the new algorithm achieved lower computational complexity and a higher interpolation speed.

Roussos-Maragos Tensor-Driven Diffusion for Image Interpolation

Abstract: Roussos and Maragos proposed a method for image interpolation in \Reversible interpolation of vectorial images by an anisotropic diusion-projecti on PDE" An earlier version was also published in conference paper (Roussos and Maragos, \Vector-Valued Image Interpolation by an Anisotropic Diusion-Projecti

Video resolution enhancement by using complex wavelet transform

Abstract: In this paper, we propose a multi-frame video resolution enhancement technique based on dual tree complex wavelet transform (DT-CWT). Here, before registration step, the respective frames have been passed through an illumination compensation procedure which is based on singular value decomposition (SVD) and discrete wavelet transform (DWT). The frame subject to the resolution enhancement is decomposed into its different frequency subbands by using DT-CWT. The high frequency subbands have been interpolated by using bicubic interpolation. Furthermore, the compensated frames are registered by using Vandewalle registration with structure adaptive normalized convolution reconstruction. Afterwards, the interpolated subbands and the output of the registration technique have been combined by using inverse DT-CWT (IDT-CWT) in order to reconstruct the super resolved frame. For Akiyo video sequence there is 5.04 dB, 4.86 dB, 5.22 dB, and 5.05 dB improvements in the average PSNR values compared to Vandewalle, Marcel, Lucchese, and Keren registration techniques, respectively.