Showing papers on "Bicubic interpolation published in 2012"

Nearest neighbor value interpolation

Abstract: This paper presents the nearest neighbor value (NNV) algorithm for high resolution (H.R.) image interpolation. The difference between the proposed algorithm and conventional nearest neighbor algorithm is that the concept applied, to estimate the missing pixel value, is guided by the nearest value rather than the distance. In other words, the proposed concept selects one pixel, among four directly surrounding the empty location, whose value is almost equal to the value generated by the conventional bilinear interpolation algorithm. The proposed method demonstrated higher performances in terms of H.R. when compared to the conventional interpolation algorithms mentioned.

Image zooming using directional cubic convolution interpolation

Abstract: Image-zooming is a technique of producing a high-resolution image from its low-resolution counterpart. It is also called image interpolation because it is usually implemented by interpolation. Keys' cubic convolution (CC) interpolation method has become a standard in the image interpolation field, but CC interpolates indiscriminately the missing pixels in the horizontal or vertical direction and typically incurs blurring, blocking, ringing or other artefacts. In this study, the authors propose a novel edge-directed CC interpolation scheme which can adapt to the varying edge structures of images. The authors also give an estimation method of the strong edge for a missing pixel location, which guides the interpolation for the missing pixel. The authors' method can preserve the sharp edges and details of images with notable suppression of the artefacts that usually occur with CC interpolation. The experiment results demonstrate that the authors'method outperforms significantly CC interpolation in terms of both subjective and objective measures.

Normalized Energy Density-Based Forensic Detection of Resampled Images

Abstract: We propose a new method to detect resampled imagery. The method is based on examining the normalized energy density present within windows of varying size in the second derivative of the image in the frequency domain, and exploiting this characteristic to derive a 19-D feature vector that is used to train a SVM classifier. Experimental results are reported on 7500 raw images from the BOSS database. Comparison with prior work reveals that the proposed algorithm performs similarly for resampling rates greater than 1, and is superior to prior work for resampling rates less than 1. Experiments are performed for both bilinear and bicubic interpolations, and qualitatively similar results are observed for each. Results are also provided for the detection of resampled imagery with noise corruption and JPEG compression. As expected, some degradation in performance is observed as the noise increases or the JPEG quality factor declines.

Improved rhombus interpolation for reversible watermarking by difference expansion

Abstract: The paper proposes an interpolation error expansion reversible watermarking algorithm. The main novelty of the paper is a modified rhombus interpolation scheme. The four horizontal and vertical neighbors are considered and, depending on their values, the interpolated pixel is computed as the average of the horizontal pixels, of the vertical pixels or of the entire set of four pixels. Experimental results are provided. The proposed scheme outperforms the results obtained by using the average on the four horizontal and vertical neighbors and the ones obtained by using well known predictors as MED or GAP.

Fast image interpolation using the bilateral filter

Abstract: In this study, the authors propose a new image interpolation technique using the bilateral filter to estimate the unknown high-resolution pixels. Compared with the least-squares estimation, a small-kernel bilateral filter has the advantages of fast computation and stability. The range distance of the bilateral filter is estimated using a novel maximum a posterior estimation, in order to consider both the diagonal and vertical–horizontal correlations. For the consideration of global consistency, the pixel-based soft-decision estimation (SAI) is proposed to constrain the consistency of edge statistic within a local window. Experimental results show that the interpolated images using the proposed algorithm give an average of 0.462, 0.413, 0.532 and 0.036 dB peak signal-to-noise ratio (PSNR) improvement compared with that using the bicubic interpolation, linear minimum mean squares error estimation, new edge-directed interpolation (NEDI) and SAI respectively. The subjective quality agrees with the PSNR as well. More importantly, the proposed algorithm is fast and it requires around 1/60 computational cost of the SAI.

Single-image super-resolution using multihypothesis prediction

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

Positive Data Visualization Using Trigonometric Function

Abstract: A piecewise rational trigonometric cubic function with four shape parameters has been constructed to address the problem of visualizing positive data. Simple data-dependent constraints on shape parameters are derived to preserve positivity and assure smoothness. The method is then extended to positive surface data by rational trigonometric bicubic function. The order of approximation of developed interpolant is .

Research on Interpolation Methods in Medical Image Processing

Abstract: Image interpolation is widely used for the field of medical image processing. In this paper, interpolation methods are divided into three groups: filter interpolation, ordinary interpolation and general partial volume interpolation. Some commonly-used filter methods for image interpolation are pioneered, but the interpolation effects need to be further improved. When analyzing and discussing ordinary interpolation, many asymmetrical kernel interpolation methods are proposed. Compared with symmetrical kernel ones, the former are have some advantages. After analyzing the partial volume and generalized partial volume estimation interpolations, the new concept and constraint conditions of the general partial volume interpolation are defined, and several new partial volume interpolation functions are derived. By performing the experiments of image scaling, rotation and self-registration, the interpolation methods mentioned in this paper are compared in the entropy, peak signal-to-noise ratio, cross entropy, normalized cross-correlation coefficient and running time. Among the filter interpolation methods, the median and B-spline filter interpolations have a relatively better interpolating performance. Among the ordinary interpolation methods, on the whole, the symmetrical cubic kernel interpolations demonstrate a strong advantage, especially the symmetrical cubic B-spline interpolation. However, we have to mention that they are very time-consuming and have lower time efficiency. As for the general partial volume interpolation methods, from the total error of image self-registration, the symmetrical interpolations provide certain superiority; but considering the processing efficiency, the asymmetrical interpolations are better.

Efficient Cubic B-spline Image Interpolation on a GPU

Abstract: Application of geometric transformation to images requires an interpolation step. When applied to image rotation, the presently most efficient GPU implementation for the cubic spline image interpolation still costs about eight times as much as linear interpolation. The implementation involves two steps: a prefilter step performs a two-pass forward-backward recursive filter, then a cubic polynomial interpolation step is implemented thanks to a cascade of linear interpolations. This article proposes a simpler and faster implementation for the prefilter—which is the most time consuming—in terms of a direct convolution. The overall cost for our cubic B-spline interpolation algorithm then reduces to 4.5 times the cost of linear interpolation.

Construction of interpolation splines minimizing semi-norm in \(W_{2}^{(m,m-1)}(0,1)\) space

Abstract: In the present paper using S.L. Sobolev’s method interpolation splines minimizing the semi-norm in a Hilbert space are constructed. Explicit formulas for coefficients of interpolation splines are obtained. The obtained interpolation spline is exact for polynomials of degree m−2 and e −x . Also some numerical results are presented.

Image Super-Resolution and Applications

Abstract: Introduction Image Interpolation Image Super-Resolution Polynomial Image Interpolation Introduction Classical Image Interpolation B-Spline Image Interpolation Polynomial Splines B-Spline Variants Nearest Neighbor Interpolation Linear Interpolation Cubic Spline Interpolation Digital Filter Implementation of B-Spline Interpolation O-MOMS Interpolation Keys' (Bicubic) Interpolation Artifacts of Polynomial Image Interpolation Ringing Aliasing Blocking Blurring Adaptive Polynomial Image Interpolation Introduction Low-Resolution Image Degradation Model Linear Space-Invariant Image Interpolation Warped-Distance Image Interpolation Weighted Image Interpolation Iterative Image Interpolation Simulation Examples Neural Modeling of Polynomial Image Interpolation Introduction Fundamentals of ANNs Cells Layers Arcs Weights Activation Rules Activation Functions Identity Function Step Function Sigmoid Function Piecewise-Linear Function Arc Tangent Function Hyperbolic Tangent Function Outputs Learning Rules Supervised Learning Unsupervised Learning Neural Network Structures Multi-Layer Perceptrons Radial Basis Function Networks Wavelet Neural Network Recurrent ANNs Training Algorithm Neural Image Interpolation Simulation Examples Color Image Interpolation Introduction Color Filter Arrays White Balance Bayer Interpolation Linear Interpolation with Laplacian Second Order Correction Adaptive Color Image Interpolation Image Interpolation for Pattern Recognition Introduction Cepstral Pattern Recognition Feature Extraction Extraction of MFCCs Framing and Windowing Discrete Fourier Transform Mel Filter Bank Discrete Cosine Transform Polynomial Coefficients Feature Extraction from Discrete Transforms Discrete Wavelet Transform Discrete Cosine Transform Discrete Sine Transform Feature Matching Using ANNs Simulation Examples Image Interpolation as Inverse Problem Introduction Adaptive Least-Squares Image Interpolation LMMSE Image Interpolation Maximum Entropy Image Interpolation Regularized Image Interpolation Simulation Examples Interpolation of Infrared Images Image Registration Introduction Applications of Image Registration Different Viewpoints (Multi-View Analysis) Different Times (Multi-Temporal Analysis) Different Sensors (Multi-Modal Analysis) Scene-to-Model Registration Steps of Image Registration Feature Detection Step Feature Matching Step Area-Based Methods Feature-Based Methods Transform Model Estimation Global Mapping Models Local Mapping Models Image Resampling and Transformation Evaluation of Image Registration Accuracy Image Fusion Introduction Objectives of Image Fusion Implementation of Image Fusion Pixel Level Image Fusion Principal Component Analysis Fusion Wavelet Fusion DWT Fusion DWFT Fusion Curvelet Fusion Sub-Band Filtering Tiling Ridgelet Transform IHS Fusion High-Pass Filter Fusion Gram-Schmidt Fusion Fusion of Satellite Images Fusion of MR and CT Images Super-Resolution with a Priori Information Introduction Multiple Observation LR Degradation Model Wavelet-Based Image Super-Resolution Simplified Multi-Channel Degradation Model Multi-Channel Image Restoration Multi-Channel LMMSE Restoration Multi-Channel Maximum Entropy Restoration Multi-Channel Regularized Restoration Simulation Examples Blind Super-Resolution Reconstruction of Images Introduction Problem Formulation Two-Dimensional GCD Algorithm 4 Blind Super-Resolution Reconstruction Approach Simulation Examples Appendix A: Discrete B-Splines Appendix B: Toeplitz-to-Circulant Approximations Appendix C: Newton's Method Appendix D: MATLAB(R) Codes References Index

Fusing soft-decision-adaptive and bicubic methods for image interpolation

Abstract: In this paper, a novel image fusion based interpolation method is proposed. Soft-decision-adaptive interpolation (SAI) is one of the state of the art image interpolation algorithms. However, SAI may produce serious artifacts in small-scale edge areas. Bicubic interpolation performs better in preserving the fidelity of small-scale edges. But, bicubic interpolation may cause zigzag and blurring artifacts around strong edges. The proposed method combines the advantages of SAI and Bicubic together through image fusion. The artifacts in the SAI interpolated image are first detected and then removed by fusing the SAI interpolated image with the bicubic interpolated image. Experiments demonstrate the effectiveness of the proposed method.

Natural cubic spline coalescence hidden variable fractal interpolation surfaces

Abstract: Fractal interpolation functions provide a new light to the natural deterministic approximation and modeling of complex phenomena. The present paper proposes construction of natural cubic spline coalescence hidden variable fractal interpolation surfaces (CHFISs) over a rectangular grid through the tensor product of univariate bases of cardinal natural cubic spline coalescence hidden variable fractal interpolation functions (CHFIFs). Natural cubic CHFISs are self-affine or non-self-affine in nature depending on the IFS parameters of univariate natural cubic spline CHFIFs. An upper bound of the error between the natural cubic spline blended coalescence fractal interpolant and the original function is deduced. Convergence of the natural cubic CHFIS to the original function , and their derivatives are deduced. The effects free variables, constrained free variables and hidden variables are discussed on the natural cubic spline CHFIS with suitably chosen examples.

Image Super-Resolution Algorithms Based on Sparse Representation of Classified Image Patches

Abstract: At present,super-resolution algorithms based on sparse representation of image patches exploit single dictionary to represent the image patches,which can not reflect the differences of various image patches types.In this paper,a novel method based on sparse representation of classified image patches is proposed to overcome this disadvantage.In this method,image patches are firstly divided into smooth patches,different directional edge patches and irregular structure patches by local features.Then these classified patches are applied into training the corresponding high and low resolution dictionary pairs.During the reconstruction process,simple bicubic interpolation approach is used for smooth patches while edge and irregular structure patches are reconstructed from their corresponding dictionary pairs using orthogonal matching pursuit algorithm.Experiment results show that the proposed algorithm significantly improves the visual quality of the edges and has faster speed compared with other single dictionary methods.

Method for reconstructing human facial image super-resolution based on similarity of facial characteristic organs

Abstract: The invention discloses a method for reconstructing human facial image super-resolution based on the similarity of facial characteristic organs. The method comprises the following steps of: 1, establishing a high-resolution front human facial image library and a high-resolution characteristic organ image library by utilizing a gray scale projection method according to a preset ideal high-resolution human facial image; 2, extracting a low-resolution characteristic organ image from a low-resolution target human facial image; 3, performing bicubic interpolation on the low-resolution target humanfacial image and the low-resolution characteristic organ image to acquire a training image set of the low-resolution image; 4, constructing characteristic space corresponding to the training image set by the training image set to reconstruct projection vectors of a corresponding high-resolution integral human facial image and a corresponding high-resolution organ image; and 5, fusing the high-resolution integral human facial image and the high-resolution characteristic organ image into a high-resolution target human facial image. The method has the characteristics of less preprocessing time, high retrieval accuracy of training images, high trueness of the acquired human facial images and the like.

Wind Speed Estimation for Iraq using several Spatial Interpolation Methods

Abstract: To decide the best method of interpolation for estimating the wind speed in Iraq region, five spatial interpolation techniques are tested (i.e. inverse distance weighted, global polynomial interpolation, local polynomial interpolation, spline with 3 sub-types, and kriging with 4 sub-types) and evaluated. Based on the root mean square error values, the predicted values are compared for period between 1971 and 2010. The results showed that the inverse distance weighted IDW yielded the best results, while the ordinary Kriging method occupied the ranked second.

Method for image interpolation using asymmetric interpolation filter and apparatus therefor

Abstract: A sub-pel-unit image interpolation method using a transformation-based interpolation filter includes, selecting, based on a sub-pel-unit interpolation location in a region supported by a plurality of interpolation filters for generating at least one sub-pel-unit pixel value located between integer-pel-unit pixels, one of a symmetric interpolation filter and an asymmetric interpolation filter from among the plurality of interpolation filters; and using the selected interpolation filter to generate the at least one sub-pel-unit pixel value by interpolating the integer-pel-unit pixels.

Adaptive image interpolation by cardinal splines in piecewise constant tension

Abstract: The cardinal spline in tension is modified to allow for different tensions in different sampling intervals. Varying the tension in proportion to an index of sharp change in image brightness, we obtain image interpolation results with less ringing artifacts compared to those by the cubic spline interpolation.

C-shaped G2 Hermite interpolation with circular precision based on cubic PH curve interpolation

Abstract: Based on the technique of C-shaped G^1 Hermite interpolation by a cubic Pythagorean-hodograph (PH) curve, we present a simple method for C-shaped G^2 Hermite interpolation by a rational cubic Bezier curve. The method reproduces a circular arc when the input data come from it. Both the Bezier control points, which have a well-understood geometrical meaning, and the weights of the resulting rational cubic Bezier curve are expressed in explicit form. We test our method with many numerical examples, and some of them are presented here to demonstrate the properties of our method. The comparison between our method and a previous method is also included.

Affine transformation registers small scale lung deformation

Abstract: To evaluate the nature of small scale lung deformation between multiple pulmonary magnetic resonance images, two different kinematic intensity based image registration techniques: affine and bicubic Hermite interpolation were tested. The affine method estimates uniformly distributed deformation metrics throughout the lung. The bicubic Hermite method allows the expression of heterogeneously distributed deformation metrics such as Lagrangian strain. A cardiac triggered inversion recovery technique was used to obtain 10 sequential images of pulmonary vessel structure in a sagittal plane in the right lung at FRC in 4 healthy subjects (Age: 28.5(6.2)). One image was used as the reference image, and the remaining images (target images) were warped onto the reference image using both image registration techniques. The normalized correlation between the reference and the transformed target images within the lung domain was used as a cost function for optimization, and the root mean square (RMS) of image intensity difference was used to evaluate the quality of the registration. Both image registration techniques significantly improved the RMS compared with non-registered target images (p= 0.04). The spatial mean (µE) and standard deviation (σ E ) of Lagrangian strain were computed based on the spatial distribution of lung deformation approximated by the bicubic Hermite method, and were measured on the order of 10−3 or less, which is virtually negligible. As a result, small scale lung deformation between FRC lung volumes is spatially uniform, and can be simply characterized by affine deformation even though the bicubic Hermite method is capable of expressing complicated spatial patterns of lung deformation.

Method for fast zooming two-dimensional seismic image based on GPU (graphic processing unit)

Abstract: The invention discloses a method for fast zooming a two-dimensional seismic image based on a GPU (graphic processing unit). The method comprises the following steps of: introducing a GPU-accelerated bicubic interpolation algorithm into an seismic color image zooming application so as to improve the zooming and interaction effects; introducing a CUDA (Compute Unified Device Architecture) and openGL (Open Graphics Library) interoperation technology into the color image zooming application so as to accelerate the display of the zoomed color image; and applying a data segmentation technology into the seismic profile color image display so as to solve the display and zooming problems of mass seismic profile data. The method disclosed by the invention has the characteristics of optimal bicubic interpolation effect, freeness from mosaics and smoothness for edge transition, fast zooming speed and capability of ensuring the real-time zooming of mass data. According to the method disclosed by the invention, the memory restriction of the adopted algorithm is much less than that of normal bicubic interpolation, so that the method is suitable for processing seismic profile images with mass data.

Analysis-suitable T-splines: characterization, refineability, and approximation

Abstract: We establish several fundamental properties of analysis-suitable T-splines which are important for design and analysis. First, we characterize T-spline spaces and prove that the space of smooth bicubic polynomials, defined over the extended T-mesh of an analysis-suitable T-spline, is contained in the corresponding analysis-suitable T-spline space. This is accomplished through the theory of perturbed analysis-suitable T-spline spaces and a simple topological dimension formula. Second, we establish the theory of analysis-suitable local refinement and describe the conditions under which two analysis-suitable T-spline spaces are nested. Last, we demonstrate that these results can be used to establish basic approximation results which are critical for analysis.

Steganography Based on Integer Wavelet Transform and Bicubic Interpolation

Abstract: Steganography is the art and science of hiding information in unremarkable cover media so as not to observe any suspicion. It is an application under information security field, being classified under information security, Steganography will be characterized by having set of measures that rely on strengths and counter attacks that are caused by weaknesses and vulnerabilities. The aim of this paper is to propose a modified high capacity image steganography technique that depends on integer wavelet transform with acceptable levels of imperceptibility and distortion in the cover image as a medium file and high levels of security. Bicubic interpolation causes overshoot, which increases acutance (apparent sharpness). The Bicubic algorithm is frequently used for scaling images and video for display. The algorithm preserves fine details of the image better than the common bilinear algorithm.

Improving empirical mode decomposition with an optimized piecewise cubic Hermite interpolation method

Abstract: Empirical mode decomposition (EMD) is an adaptive method for analyzing non-stationary time series derived from linear and nonlinear systems. But the upper and lower envelopes fitted by cubic spline (CS) interpolation may often occur overshoots. In this paper, a novel envelope fitting method based on the optimized piecewise cubic Hermite (OPCH) interpolation is developed. Taking the difference between extreme as the cost function, chaos particle swarm optimization (CPSO) method is used to optimize the derivatives of the interpolation nodes. The flattest envelope with the optimized derivatives can overcome the overshoots well. Some numerical experiments conclude this paper, and comparisons are carried out with the classical EMD.

Fast Refrigerant Property Calculations Using Interpolation-Based Methods

Abstract: An interpolation-based method for computing refrigerant properties which can be used in simulations of vaporcompression air-conditioning equipment is presented. This method uses bicubic functions to interpolate between samples of the Helmholtz energy surface as a function of temperature and density. Three beneficial characteristics of this method are discussed: speed, accuracy, and consistency, as well as a means for using a variety of different independent variables are also presented. The implementation of this method is discussed, and experimental results for property calculations for refrigerant R-134a are prese nted which compare favorably to standard Newton-based equation of state methods.

Principal Components Analysis-Based Edge-Directed Image Interpolation

Abstract: This paper presents an edge-directed, noniterative image interpolation algorithm. In the proposed algorithm, the gradient directions are explicitly estimated with a statistical-based approach. The local dominant gradient directions are obtained by using principal components analysis (PCA) on the four nearest gradients. The angles of the whole gradient plane are divided into four parts, and each gradient direction falls into one part. Then we implement the interpolation with one-dimention (1-D) cubic convolution interpolation perpendicular to the gradient direction. Compared to the state of-the-art interpolation methods, simulation results show that the proposed PCA-based edge-directed interpolation method preserves edges well while maintaining a high PSNR value.