Showing papers on "Bicubic interpolation published in 2008"

Image Interpolation by Adaptive 2-D Autoregressive Modeling and Soft-Decision Estimation

Abstract: The challenge of image interpolation is to preserve spatial details. We propose a soft-decision interpolation technique that estimates missing pixels in groups rather than one at a time. The new technique learns and adapts to varying scene structures using a 2-D piecewise autoregressive model. The model parameters are estimated in a moving window in the input low-resolution image. The pixel structure dictated by the learnt model is enforced by the soft-decision estimation process onto a block of pixels, including both observed and estimated. The result is equivalent to that of a high-order adaptive nonseparable 2-D interpolation filter. This new image interpolation approach preserves spatial coherence of interpolated images better than the existing methods, and it produces the best results so far over a wide range of scenes in both PSNR measure and subjective visual quality. Edges and textures are well preserved, and common interpolation artifacts (blurring, ringing, jaggies, zippering, etc.) are greatly reduced.

Indusion: Fusion of Multispectral and Panchromatic Images Using the Induction Scaling Technique

Abstract: The fusion of multispectral (MS) and panchromatic (PAN) images is a useful technique for enhancing the spatial quality of low-resolution MS images. Liu recently proposed the smoothing-filter-based intensity modulation (SFIM) fusion technique. This technique upscales MS images using bicubic interpolation and introduces high-frequency information of the PAN image into the MS images. However, this fusion technique is plagued by blurred edges if the upscaled MS images are not accurately coregistered with the PAN image. In the first part of this letter, we propose the use of the Induction scaling technique instead of bicubic interpolation to obtain sharper, better correlated, and hence better coregistered upscaled images. In the second part, we propose a new fusion technique derived from induction, which is named ldquoIndusion.rdquo In this method, the high-frequency content of the PAN image is extracted using a pair of upscaling and downscaling filters. It is then added to an upscaled MS image. Finally, a comparison of SFIM (with both bicubic interpolation and induction scaling) is presented along with the fusion results obtained by IHS, discrete wavelet transform, and the proposed Indusion techniques using Quickbird satellite images.

Hiding Traces of Resampling in Digital Images

Abstract: Resampling detection has become a standard tool for forensic analyses of digital images. This paper presents new variants of image transformation operations which are undetectable by resampling detectors based on periodic variations in the residual signal of local linear predictors in the spatial domain. The effectiveness of the proposed method is supported with evidence from experiments on a large image database for various parameter settings. We benchmark detectability as well as the resulting image quality against conventional linear and bicubic interpolation and interpolation with a sinc kernel. These early findings on ldquocounter-forensicrdquo techniques put into question the reliability of known forensic tools against smart counterfeiters in general, and might serve as benchmarks and motivation for the development of much improved forensic techniques.

A New Method for Registration-Based Medical Image Interpolation

Abstract: A new technique is presented for interpolating between grey-scale images in a medical data set. Registration between neighboring slices is achieved with a modified control grid interpolation algorithm that selectively accepts displacement field updates in a manner optimized for performance. A cubic interpolator is then applied to pixel intensities correlated by the displacement fields. Special considerations are made for efficiency, interpolation quality, and compression in the implementation of the algorithm. Experimental results show that the new method achieves good quality, while offering dramatic improvement in efficiency relative to the best competing method.

Fast Artifacts-Free Image Interpolation.

Abstract: In this paper we describe a novel general purpose image interpolation method based on the combination of two different procedures. First, an adaptive algorithm is applied interpolating locally pixel values along the direction where second order image derivative is lower. Then interpolated values are modified using an iterative refinement minimizing differences in second order image derivatives, maximizing second order derivative values and smoothing isolevel curves. The first algorithm itself provides edge preserving images that are measurably better than those obtained with similarly fast methods presented in the literature. The full method provides interpolated images with a ”natural” appearance that do not present the artifacts affecting linear and nonlinear methods. Objective and subjective tests on a wide series of natural images clearly show the advantages of the proposed technique over existing approaches.

G 2 tensor product splines over extraordinary vertices

Abstract: We present a second order smooth filling of an n-valent Catmull-Clark spline ring with n biseptic patches. While an underdetermined biseptic solution to this problem has appeared previously, we make several advances in this paper. Most notably, we cast the problem as a constrained minimization and introduce a novel quadratic energy functional whose absolute minimum of zero is achieved for bicubic polynomials. This means that for the regular 4-valent case, we reproduce the bicubic B-splines. In other cases, the resulting surfaces are aesthetically well behaved. We extend our constrained minimization framework to handle the case of input mesh with boundary.

The efficient VLSI design of BI-CUBIC convolution interpolation for digital image processing

Abstract: This paper presents an efficient VLSI design of bicubic convolution interpolation for digital image processing. The architecture of reducing the computational complexity of generating coefficients as well as decreasing number of memory access times is proposed. Our proposed method provides a simple hardware architecture design, low computation cost and is easy to implement. Based on our technique, the high-speed VLSI architecture has been successfully designed and implemented with TSMC 0.13 mum standard cell library. The simulation results demonstrate that the high performance architecture of bi-cubic convolution interpolation at 279 MHz with 30643 gates in a 498times498 mum chip is able to process digital image scaling for HDTV in real-time.

Morphological shape decomposition interframe interpolation method

Abstract: One of the main image representations in mathematical morphology is the shape decomposition representation, useful for image compression and pattern recognition. The morphological shape decomposition representation can be generalized to extend the scope of its algebraic characteristics as much as possible. With these generalizations, the morphological shape decomposition (MSD) role to serve as an efficient image decomposition tool is extended to interpolation of images. We address the binary and grayscale interframe interpolation by means of generalized morphological shape decomposition. Computer simulations illustrate the results.

Natural bicubic spline fractal interpolation

Abstract: Fractal Interpolation functions provide natural deterministic approximation of complex phenomena Cardinal cubic splines are developed through moments (ie second derivative of the original function at mesh points) Using tensor product, bicubic spline fractal interpolants are constructed that successfully generalize classical natural bicubic splines An upper bound of the difference between the natural cubic spline blended fractal interpolant and the original function is deduced In addition, the convergence of natural bicubic fractal interpolation functions towards the original function providing the data is studied

Method for automatically split-jointing wide-angle image

Abstract: A wide-angle image automatic joining method comprises: a step one of setting a moving speed of a wide-angle camera and an image collecting period; a step two of obtaining a superposition pixel width of two adjacent images; a step three of building an image non-linear aberration correcting mathematical model; a step four of creating an image data coordinate conversion relationship; a step five of calculating an aberration vector parameter and an outer parameter value; a step six of performing an inversion operation on the image with aberration to obtain the whole real image after correcting the non-linear aberration; a step seven of rapidly finding common characteristics of the two adjacent images in the superposition width of the images with corrected non-linear aberration; a step eight of realizing registration of the two images by perspective conversion; a step nine of joining the two registered images; a step ten of inosculating the joining position of the images based on a wavelet bicubic interpolation searching method. According to the invention, continuous inosculation and seamless join of the adjacent images can be automatically realized.

Interpolation Models for Image Super-resolution

Abstract: Image super-resolution involves interpolating a non-uniformly sampled composite image at uniform locations of a high-resolution image. Interpolation methods used in the literature are generally based on arbitrary functions. Optimal (in least squares sense) interpolation kernels can be derived if the ground-truth high-resolution data is known, this is obviously impractical. An observation that the optimal kernels for very different images are similar suggests that a kernel derived on one image can interpolate another image with good results. This paper extends this idea by developing two image models that capture the important characteristics of an image and uses the models to derive optimal kernels. One of the models results in linear interpolation and the other one results in a piece-wise cubic kernel similar to that of cubic spline. This later model is experimentally shown to be near optimal for three different images. The notion of deriving optimal interpolators from the image model and the model of image capturing process provides a unifying framework that brings together linear and cubic interpolators and gives them a theoretic backing.

A new approach for data partitioning through high dimensional model representation

Abstract: A multivariate function f(x1,..., xN) can be evaluated via interpolation if its values are given at a finite number nodes of a hyperprismatic grid in the space of independent variables x1, x2,..., xN. Interpolation is a way to characterize an infinite data structure (function) by a finite number of data approximately. Hence it leaves an infinite arbitrariness unless a mathematical structure with finite number of flexibilities is imposed for the unknown function. Imposed structure has finite dimensionality. When the dimensionality increases unboundedly, the complexities grow rapidly in the standard methods. The main purpose here is to partition the given multivariate data into a set of low-variate data by using high dimensional model representation (HDMR) and then, to interpolate each individual data in the set via Lagrange interpolation formula. As a result, computational complexity of the given problem and needed CPU time to obtain the results through a series of programs in computers decrease.

Spatial interpolation algorithm for error concealment

Abstract: In this paper, we propose a new spatial interpolation algorithm for intra-frame error concealment. The method aims at interpolating areas in the image, which have been affected by packet loss. We have proposed an edge detection technique to aid the bilinear interpolation. The edge-detection scheme is based on designing a robust Hough transform-based technique that is capable of systematically connecting edges irrespective of the number of edge points surrounding missing areas. The connected edges are used to divide the missing areas into different regions for interpolation along the directions of each detected line. Simulation results demonstrate that the proposed algorithm can recover the missing areas with a greater accuracy, when compared with the bilinear interpolation technique.

Performances of feature tracking in turbulent boundary layer investigation

Abstract: In this paper, we describe the application of a feature tracking (FT) algorithm for the measurement of velocity statistics in a turbulent boundary layer over a flat plate at Re θ ≃ 3,700. The feature tracking algorithm is based on an optical flow approach. Displacements are obtained by searching the parameters of the mapping between interrogation windows in the first and second image which minimize a correlation distance between them. The correlation distance is here defined as the minimum of the sum of squared differences of interrogation windows intensities. The linearized equation which governs the minimization problem is solved with an iterative procedure only where the solution is guaranteed to exist, thus maximizing the signal-to-noise ratio. In this process, the interrogation window first undergoes a pure translation, and then a complete affine deformation. Final mapping parameters provide the velocity and velocity gradients values in a lagrangian framework. The interpolation inherent to window-deforming algorithms represents a critical factor for the overall accuracy and particular attention must be devoted to this step. In this paper different schemes are tested, and their effects on algorithm accuracy are first discussed by looking at the distribution of systematic and random errors computed from synthetic images. The same analysis is then performed on the turbulent boundary layer data, where the effects associated with the use of a near-wall logical mask are also investigated. The comparison with single-point data gathered from the literature demonstrate the overall ability of the FT technique to correctly extract all relevant statistical quantities, including the spanwise vorticity distribution. Concerning the mean velocity profile, no evident influence of the interpolation scheme appears, while the near-wall accuracy is improved by the application of the logical mask. On the contrary, for the fluctuating components of the velocity, the interpolation based on B-Spline basis functions is found to perform better compared to the classical Bicubic scheme, particularly in the highly sheared region close to the wall.

A comparison of superresolution reconstruction methods for multi-angle CHRIS/Proba images

Abstract: Compact High Resolution Imaging Spectrometer onboard the Project for On-board Autonomy, or CHRIS/Proba, represents a new generation of satellite images that provide different acquisitions of the same scene at five different angles. Given the hyperspectral-oriented waveband configuration of the CHRIS images, the scope of its application would be much wider if the present 17m nadir resolution could be refined. This paper presents the results of three superresolution methods applied to multiangular CHRIS/Proba data. The CHRIS images were preprocessed and then calibrated into reflectance using the method described in [1][2]. Automatic registration using an intensity variation approach described in [3] was implemented for motion estimation. Three methods, namely non-uniform interpolation and de-convolution [4], iterative back-projection [5], and total variation [6] are examined. Quantitative measures including peak signal to noise ratio [7], structural similarity [8], and edge stability [9], are used for the evaluation of the image quality. To further examine the benefit of multi-frame superresolution methods, a single-frame superresolution method of bicubic resampling was also applied. Our results show that a high resolution image derived from superresolution methods enhance spatial resolution and provides substantially more image details. The spectral profiles of selected land covers before and after the application of superresolution show negligible differences, hinting the use of superresolution algorithm would not degrade the capability of the data set for classification. Among the three methods, total variation gives the best performance in all quantitative measures. Visual inspections find good results with total variation and iterative back-projection approaches. The use of superresolution algorithms, however, is complex as there are many parameters. In this paper, most of the parameter settings were tuned manually or decided empirically.

An Image Scaling Algorithm Based on Bilinear Interpolation with VC

Abstract: Digital image scaling is widely used in a variety of areas.But the use of the function StretchBlt to enlarge and shrink the digital image often causes distortion.A bilinear interpolation is proposed to solve this problem and can significant increase the clarity of the image.

Investigation on several basic interpolation methods for the use in remote sensing application

Abstract: Image from satellite is an example of remote sensing data. However, when the resolution of the available satellite image is too coarse and does not meet the required resolution, a process known as image re-sampling need to be employed, so a higher resolution version of the image could be obtained. Image re-sampling may involve interpolation, which is a process of allocating intensity value into a new generated pixel. Yet, interpolation method usually degrades the image quality. In this paper, five basic interpolation methods have been successfully implemented. These interpolation methods are nearest neighbor interpolation, bilinear interpolation, interpolation with smoothing filter, interpolation with sharpening filter, and interpolation with unsharp masking. The aim of this project is to find interpolation method that is suitable for remote sensing data. The method of our interest is the method that is easy to be implemented, but can preserve the quality of the data in term of sharpness and validness of the information. Based on the results, it is shown that all five interpolation methods tested in this research can produce good quality output when the resolution of input image is high. For low resolution input, only bilinear, smoothing filter and unsharp masking can preserve the quality of the image. However, this is only limited for interpolation with magnification factor less than 5. Bilinear, smoothing filter and unsharp masking are suitable to interpolate remote sensing data if the resolution of the input image is high enough.

Interpolation Methods for the SRP-PHAT Algorithm

Abstract: The steered response power-phase transform (SRP-PHAT) algorithm is known to be one of the state-of-the-art methods in acoustical source localization (ASL) and it has been shown to outperform other traditional ASL methods such as the steered beamformer-based (SBF) method. The accuracy of the SRP-PHAT algorithm is limited by the time resolution of the PHAT weighted cross correlation functions — the basic building blocks of the SRP-PHAT algorithm. In this article, three methods for interpolating the cross correlation functions of the SRP-PHAT are compared with real concert hall data. Two of the methods are build from previously introduced assumptions on the shape of the global maximum of the cross correlation function. In the experiments it is shown that certain limitations of the SRP-PHAT algorithm can be compensated with interpolation.

An Improved Multilevel Green's Function Interpolation Method With Adaptive Phase Compensation

Abstract: An improved multilevel Green's function interpolation method (MLGFIM) with adaptive phase compensation (APC) is proposed. The difficulty in applying interpolation approaches to the fast varying phase term in the integral equation kernel for full-wave electromagnetic (EM) simulations is eradicated by using the phase compensation and adaptive direction separation (ADS). The multilevel tree structure in MLGFIM keeps the number of direction separation invariant at all levels, attributing to the recursive interpolation with multilevel phase compensation. The proposed MLGFIM-APC in conjunction with the Lagrange-Chebyshev interpolation yield an O(N log N) CPU time and O(N) computer memory requirement for surface scattering problems. By introducing a transition level, the MLGFIM-APC can adaptively incorporate interpolation techniques of conventional interpolation (CI), transition interpolation (TI) and phase-compensation interpolation (PI), corresponding to electromagnetic simulation of problems of small, moderate, and large electrical sizes, respectively. Large-scale microstrip antenna arrays are simulated to illustrate the accuracy and efficiency of the proposed method. It is found that the CPU time scales better than O(N log N) for these co-planar problems.

Improved interpolation method based on singular spectrum analysis iteration and its application to missing data recovery

Abstract: A novel interval quartering algorithm (IQA) is proposed to overcome insufficiency of the conventional singular spectrum analysis (SSA) iterative interpolation for selecting parameters including the number of the principal components and the embedding dimension. Based on the improved SSA iterative interpolation, interpolated test and comparative analysis are carried out to the outgoing longwave radiation daily data. The results show that IQA can find globally optimal parameters to the error curve with local oscillation, and has advantage of fast computing speed. The improved interpolation method is effective in the interpolation of missing data.

Interpolation of Irregular Data

Abstract: Implementations described herein provide methods and devices for interpolating or estimating data from previously acquired data. Furthermore, implementations described herein calculate optimum interpolation operators by maximizing the spatial bandwidth of interpolation operators within a specified acceptable mean square error. According to one implementation, spatial bandwidth may be maximized by selecting a local grid within a global grid having nodes corresponding to desired interpolation locations. According to another implementation, spatial bandwidth may be maximized by specifying maximum wave numbers when calculating interpolation operators.

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

Introduction to Optimal Interpolation and Variational Analysis

Abstract: Contents 1 Theory of optimal interpolation and variational analysis 2 1. A Some matrix identities 34 References 35 1 Chapter 1 Theory of optimal interpolation and variational analysis Contents

Downsampling-based multiple description image coding using optimal filtering

Abstract: In this paper, a multiple description image coding scheme is proposed to facilitate the transmission of images over media with possible packet loss. The proposed method is based on finding the optimal reconstruction filter coefficients that will be used to reconstruct lost descriptions. For this purpose initially, the original image is downsampled and each subimage is coded using standard JPEG. These decoded images are then mapped to the original im- age size using the optimal filters. Multiple descriptions consist of coded down-sampled images and the corresponding optimal recon- struction filter coefficients. It is shown that the proposed method provided better results compared to standard interpolation filters (i.e., bicubic and bilinear). © 2008 SPIE and IS&T.

Bicubic G 1 interpolation of irregular quad meshes using a 4-split

Abstract: We present a piecewise bi-cubic parametric G1 spline surface interpolating the vertices of any irregular quad mesh of arbitrary topological type. While tensor product surfaces need a chess boarder parameterization they are not well suited to model surfaces of arbitrary topology without introducing singularities. Our spline surface consists of tensor product patches, but they can be assembled with G1-continuity to model any non-tensor-product configuration. At common patch vertices an arbitrary number of patches can meet. The parametric domain is built by 4-splitting one unit square for each input quadrangular face. This key idea of our method enables to define a very low degree surface, that interpolates the input vertices and results from an explicit and local procedure : no global linear system has to be solved.