Image Interpolation

This paper proposes an edge-directed interpolation algorithm for natural images. The basic idea is to first estimate local covariance coefficients from a low-resolution image and then use these covariance estimates to adapt the interpolation at a higher resolution based on the geometric duality between the low-resolution covariance and the high-resolution covariance. The edge-directed property of covariance-based adaptation attributes to its capability of tuning the interpolation coefficients to match an arbitrarily oriented step edge. A hybrid approach of switching between bilinear interpolation and covariance-based adaptive interpolation is proposed to reduce the overall computational complexity. Two important applications of the new interpolation algorithm are studied: resolution enhancement of grayscale images and reconstruction of color images from CCD samples. Simulation results demonstrate that our new interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional linear interpolation.

We present a new method based on B-spline snakes (active contours) for measuring high-accuracy contact angles. In this approach, we avoid making physical assumptions by defining the contour of the drop as a versatile B-spline curve. When useful, we extend this curve by mirror symmetry so that we can take advantage of the reflection of the drop onto the substrate to detect the position of the contact points. To keep a wide range of applicability, we refrain from discretizing the contour of the drop, and we choose to optimize an advanced image-energy term to drive the evolution of the curve. This term has directional gradient and region-based components; additionally, another term-an internal energy-is responsible for the snake elasticity and constrains the parameterization of the spline. While preserving precision at the contact points, we limit the computational complexity by constraining a non-uniform repartition of the control points. The elasticity property of the snake links the local nature of the contact angle to the global contour of the drop. A global knowledge of the drop contour allows us to use the reflection of the drop on the substrate to automatically and precisely detect a line of contact points (vertical position and tilt). We apply cubic-spline interpolation over the image of the drop; then, the evolution procedure takes part in this continuous domain to avoid the inaccuracies introduced by pixelization and discretization.

Nonparametric Bayesian methods are considered for recovery of imagery based upon compressive, incomplete and/or noisy measurements. A truncated beta-Bernoulli process is employed to infer an appropriate dictionary for the data under test, and also for image recovery. In the context of compressive sensing, significant improvements in image recovery are manifested using learned dictionaries, relative to using standard orthonormal image expansions. The compressive-measurement projections are also optimized for the learned dictionary. Additionally, we consider simpler (incomplete) measurements, defined by measuring a subset of image pixels, selected uniformly at random; connections are made to matrix completion and union-of-subspace models, providing a link between matrix completion and image processing. Spatial inter-relationships within imagery are exploited through use of the Dirichlet and probit stick-breaking processes. Several example results are presented, with comparisons to other methods in the literature.

We discuss possible algorithms for interpolating data given in a set of curves and/or points in the plane. We propose a set of basic assumptions to be satis ed by the interpolation algorithms which lead to a set of models in terms of possibly degenerate elliptic partial di erential equations. The Absolute Minimal Lipschitz Extension model (AMLE) is singled out and studied in more detail. We show experiments suggesting a possible application, the restoration of images with poor dynamic range.

Satellite images are being used in many fields of research. One of the major issues of these types of images is their resolution. In this paper, we propose a new satellite image resolution enhancement technique based on the interpolation of the high-frequency subbands obtained by discrete wavelet transform (DWT) and the input image. The proposed resolution enhancement technique uses DWT to decompose the input image into different subbands. Then, the high-frequency subband images and the input low-resolution image have been interpolated, followed by combining all these images to generate a new resolution-enhanced image by using inverse DWT. In order to achieve a sharper image, an intermediate stage for estimating the high-frequency subbands has been proposed. The proposed technique has been tested on satellite benchmark images. The quantitative (peak signal-to-noise ratio and root mean square error) and visual results show the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques.

In Magnetic Resonance Imaging, image resolution is limited by several factors such as hardware or time constraints. In many cases, the acquired images have to be upsampled to match a specific resolution. In such cases, image interpolation techniques have been traditionally applied. However, traditional interpolation techniques are not able to recover high frequency information of the underlying high resolution data. In this paper, a new upsampling method is proposed to recover some of this high frequency information by using a data-adaptive patch-based reconstruction in combination with a subsampling coherence constraint. The proposed method has been evaluated on synthetic and real clinical cases and compared with traditional interpolation methods. The proposed method is shown to outperform classical interpolation methods compared in terms of quantitative measures and visual observation.

An application of insect visuomotor behavior to automatic control of landing is explored. Insects, being perhaps more reliant on image motion cues than mammals or higher vertebrates, are proving to be an excellent organism in which to investigate how information on optic flow is exploited to guide locomotion and navigation. We have observed how bees perform grazing landings on a flat surface and have deduced the algorithmic basis for the behavior. A smooth landing is achieved by a surprisingly simple and elegant strategy: image velocity is held constant as the surface is approached, thus automatically ensuring that flight speed is close to zero at touchdown. No explicit knowledge of flight speed or height above the ground is necessary. The feasibility of this landing strategy was tested by implementation in a robotic gantry. We also outline our current efforts at exploring the applicability of this and related techniques to the guidance of uninhabited airborne vehicles (UAVs). Aspects of the algorithm were tested on a small UAV using real imagery to control descent rate.

In this paper we analyse the problem of general purpose image upscaling that preserves edge features and natural appearance and we present the results of subjective and objective evaluation of images interpolated using different algorithms. In particular, we consider the well-known NEDI (New Edge Directed Interpolation, Li and Orchard, 2001) method, showing that by modifying it in order to reduce numerical instability and making the region used to estimate the low resolution covariance adaptive, it is possible to obtain relevant improvements in the interpolation quality. The implementation of the new algorithm (iNEDI, improved New Edge Directed Interpolation), even if computationally heavy (as the Li and Orchard's method), obtained, in both subjective and objective tests, quality scores that are notably higher than those obtained with NEDI and other methods presented in the literature.

We present a modification of the new edge-directed interpolation method that eliminates the prediction error accumulation problem by adopting a modified training window structure, and extending the covariance matching into multiple directions to suppress the covariance mismatch problem. Simulation results show that the proposed method achieves remarkable subjective performance in preserving the edge smoothness and sharpness among other methods in the literaturé. It also demonstrates consistent objective performance among a variety of images.

Damage in materials can be represented in many forms such as specific void or crack surfaces, the spacing between cracks, scalar representation of damage and general tensorial representation of damage. This paper presents methods to quantify the specific damaged surface area, the specific damaged surface area tensor, the damage tensor, the mean solid path among the damaged surfaces and the mean solid path tensor. The methods are general and use the reconstructed three-dimensional structure from tomography images and a virtual sectioning technique to obtain cross-sectional images needed for the quantification. The paper also presents the relation between the quantified damage parameters and their applications in mechanical modeling. This work is applied in particular to quantify the damage parameters of asphalt concrete specimens of the WesTrack project.

Standard methods for image interpolation are based on smoothly fitting the image intensity surface. Previous edge-directed interpolation methods add limited geometric information (edge maps) to build more accurate and visually appealing interpolations at key contours in the image. This paper presents a method for geometry-based interpolation that smoothly fits the isophote (intensity level curve) contours at all points in the image rather than just at selected contours. By using level set methods for curve evolution, no explicit extraction or representation of these contours is required (unlike earlier edge-directed methods). The method uses existing interpolation techniques as an initial approximation and then iteratively reconstructs the isophotes using constrained smoothing. Results show that the technique produces results that are more visually realistic than standard function-fitting methods

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.

The total cavopulmonary connection (TCPC) is a palliative surgical repair performed on children with a single ventricle (SV) physiology. Much of the power produced by the resultant single ventricle pump is consumed in the systemic circulation. Consequently the minimization of power loss in the TCPC is imperative for optimal surgical outcome. As a component of a comprehensive surgical planning and evaluation tool we have developed a method of vascular morphology reconstruction based on adaptive control grid interpolation to function as a precursor to computational fluid dynamics (CFD) analysis aimed at quantifying power loss. Our technique combines positive aspects of optical flow-based and block-based motion estimation algorithms to accurately reconstruct vascular geometries with a minimal degree of computational complexity. Subsequent CFD simulations offer the pressure and velocity information necessary to quantify power loss in the TCPC on a pre and post-operative basis. Collectively these steps form a powerful tool for both surgical planning and evaluation aimed at producing optimal TCPC configurations for successful surgical outcomes. Both reconstruction and CFD components of the technique will be discussed.

Image morphing, or image interpolation in the time domain, deals with the metamorphosis of one image into another. In this paper, a new class of image morphing algorithms is proposed based on the theory of optimal mass transport. The 2 mass moving energy functional is modified by adding an intensity penalizing term, in order to reduce the undesired double exposure effect. It is an intensity-based approach and, thus, is parameter free. The optimal warping function is computed using an iterative gradient descent approach. This proposed morphing method is also extended to doubly connected domains using a harmonic parameterization technique, along with finite-element methods.

B-splines are commonly used for continuous representation of discrete time signals. This kind of representation proves to be very useful in applications such as image interpolation, rotation and edge detection. In all these applications, the first step is to compute the B-spline coefficients of the signal, and this involves the use of an IIR noncausal filter called the direct B-spline filter. The signal reconstruction is achieved using the indirect B-spline filter, which in many applications operates at a higher rate.

Conventional image interpolation techniques degrade quality of the magnified image due to various artifacts, such as, blockimg artifact and excessive smoothing. Those degradations become worse as the magnification ratio increases. And there also exists tradeoff between reducing blocking artifact and excessive smoothness.

A feature-guided image interpolation scheme is presented. It is an effective and improved, shape-based interpolation method used for interpolating image slices in medical applications. The proposed method integrates feature line-segments to guide the shape-based method for better shape interpolation. An automatic method for finding these line segments is given. The proposed feature-guided shape-based method can manage translation, rotation and scaling situations when the slices have similar shapes. It can also interpolate intermediate shapes when the successive slices do not have similar shapes. This method is experimentally evaluated using artificial and real two-dimensional and three-dimensional data. The proposed method generated satisfactory interpolated results in these experiments. We demonstrate the practicality, effectiveness and reproducibility of the proposed method for interpolating medical images.

Two digital filters ( ) and ( ) are said to be biorthogonal partners of each other if their cascade ( ) ( ) satisfies the Nyquist or zero-crossing property. Biorthogonal partners arise in many different contexts such as filterbank theory, exact and least squares digital interpolation, and multiresolution theory. They also play a central role in the theory of equalization, especially, fractionally spaced equalizers in digital communications. In this paper, we first develop several theoretical properties of biorthogonal partners. We also develop conditions for the existence of biorthogonal partners and FIR biorthogonal pairs and establish the connections to the Riesz basis property. We then explain how these results play a role in many of the above-mentioned applications.

ABSTRACT In this paper the authors propose a method to use interpolation to eliminate characters in only one image with telops or other text through an image interpolation method that uses the eigenspace method. Background scenes and other images have a fractal character, and often the self-correlation in the image can be assumed to be high. The authors focus on this point and represent rules for describing the image based on an eigenspace consisting of only one image that has defects. The eigenspace generated in this manner reflects the features of the image, and by using this eigenspace, image interpolation can be achieved. Although this interpolation method does not restore the original image, the authors confirmed through experimental results that it can provide interpolation without a feeling of oddness for images which have a high level of self-correlation. © 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 38(1): 87– 96, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10319.

This paper proposes a selective data pruning-based compression scheme to improve the rate-distortion relation of compressed images and video sequences. The original frames are pruned to a smaller size before compression. After decoding, they are interpolated back to their original size by an edge-directed interpolation method. The data pruning phase is optimized to obtain the minimal distortion in the interpolation phase. Furthermore, a novel high-order interpolation is proposed to adapt the interpolation to several edge directions in the current frame. This high-order filtering uses more surrounding pixels in the frame than the fourth-order edge-directed method and it is more robust. The algorithm is also considered for multiframe-based interpolation by using spatio-temporally surrounding pixels coming from the previous frame. Simulation results are shown for both image interpolation and coding applications to validate the effectiveness of the proposed methods.

In this paper, for imaging applications, we introduce partial differential equations (PDEs), which allow for correcting displacement errors, for dejittering, and for deinterlacing, respectively, in multi-channel data. These equations are derived via semi-groups for non-convex energy functionals. As a particular example, for gray valued data, we find the mean curvature equation and the corresponding non-convex energy functional. As a further application for correction of displacement errors we study image interpolation, in particular zooming, of digital color images. For actual image zooming, the solutions of the proposed PDEs are projected onto a space of functions satisfying interpolation constraints. A comparison of the test results with standard and state-of-the-art interpolation algorithms shows the competitiveness of this approach.

This paper presents a regularized iterative image interpolation algorithm, which can restore high €requency details in the original high resolution image. In order to apply the regularization approach to the interpolation procedure, we first present a two-dimensional separable image degradation model for a low resolution imaging system. According to the model, we propose a regularization based spatial image sequence interpolation algorithm and apply the proposed algorithm to a spatially scalable coding.

In this modern age in which we are living, digital images play a vital role in many application areas. But at the same time the image retouching techniques has also increased which forms a serious threat to the security of digital images. To cope with this problem, the field of digital forensics and investigation has emerged and provided some trust in digital images. In this paper we proposed a technique for image authentication that detects the manipulations that are done in the digital images. In most of the image forgeries such as copy-and-paste forgery, region duplication forgery, image splicing forgery etc basic image operations or manipulations are often involved. Thus if there exists the evidence for basic image alterations in digital images we can say that the image has been altered. This paper aims at detecting the basic image operations such as re-sampling (rotation, rescaling), contrast enhancement and histogram equalization which are often done in forged images. The available interpolation related spectral signature method is used for detecting rotation and rescaling and for estimating parameters such as rotation angle and rescale factors. This rotation/rescaling detection method detects some unaltered images as altered one when the images are JPEG compressed. We have overcome that problem by adding noise in the input images. We have also used the existing fingerprint detection technique for detecting contrast enhancement and histogram equalization. Besides the techniques discussed in the existing method, we identified a unique property for histogram equalization which can help us to differentiate contrast enhancement from histogram equalization. This work is tested in USC-SIPI database which consists of general unaltered images and achieved results with satisfactory accuracy. ª

A key step in distributed video coding is the generation of the side information (SI) i.e. the estimation of the Wyner-Ziv frame (WZF). This step is also frequently called image interpolation. State-of-the-art techniques perform a motion estimation between adjacent key frames (KFs) and linear interpolation in order to assess object positions in the WZF, and then the SI is produced by motion compensating the KFs. However the uniform motion model underlying this approach is not always able to produce a satisfying estimation of the motion, which can result in a low SI quality.

Due to the harsh conditions of space, the detectors within satellite-based multispectral imagers are always at risk of damage or failure. In particular, 15 out of the 20 detectors that produce the 1.6-µm band 6 of Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua are either dead or noisy. In this paper, we describe a quantitative image restoration (QIR) algorithm that is able to accurately estimate and restore the data lost due to multiple-detector failure. The small number of functioning detectors is used to train a restoration function that is based on a multivariate regression using the information in a spatial-spectral window around each restored pixel. The information from other spectral bands allows QIR to perform well even when standard image interpolation breaks down due to large contiguous sections of the image being missing, as is the case for MODIS band 6 on Aqua. We present a comprehensive evaluation of the QIR algorithm by simulating the Aqua damage using the working 1.6-µm band of MODIS on Terra and then comparing the QIR restoration to the original (unbroken) Terra image. We also compare our results with other researchers' prior work that has been based on the assumption that band 6 could be approximated well solely as a function of the related band 7. We present empirical evidence that there is information in the other 500-and 250-m bands, excluding bands 6 and 7, that can inform the estimation of missing band 6 data. We demonstrate superior performance of QIR over previous algorithms as reflected by a reduced root-mean-square-error evaluation. The QIR algorithm may also be adapted to other cases and provides a powerful and general algorithm to mitigate the risks of detector damage in multispectral remote sensing.

This paper presents a new image interpolation approach based on variational principles. The image interpolation problem is formulated as the constrained minimization of a certain functional. The choice of the functional to be minimized is based on the combination of the restrictions imposed by the mathematical problem and certain considerations related to the physical problem. This paper focuses on the class of nonnegative quadratic functionals whose minimization amounts to 2-D Lgeneralized splines. The partial differential operators used correspond to certain stochastic partial differential equation (SPDE) image models. Image interpolation is exactly formulated in its discrete form and an extensive analytical treatment of the resulting minimization problem is subsequently presented. The implementation difficulties and computational requirements indicate that such a formulation cannot provide interpolation algorithms of practical value. The derivation of practical interpolation algorithms is based on an alternative formulation of the optimization problem. This new formulation allows the transformation of the original constrained minimization into an equivalent unconstrained one. A number of interpolation algorithms is proposed, based on various noncausal and semicausal SPDE image models. Finally, experimental results are presented, compared and discussed. Zusammenf~g. Der Beitrag beschreibt eine auf die Variationsrcchnung aufbauende neue Methode zur Bildinterpolation. Die Bildinterpolation wird formuliert als Minimierung eines Funktionals unter Beachtung von Nebenbcdingungen. Die Auswahl des zu minimierenden Funktionals baut auf auf eine Kombination von Restriktionen, die durch das mathematische Problem gegeben sind, und gewissen physikalischen oberlegungen. Der Beitrag konzentriert sich auf die Klasse nichtnegativer quadratischer Funktionale, deren Minimierung auf 2-D Lverallgemeinerte Splines fiihrt. Die verwendeten partiellen Differentialoperatoren gehiiren zu Bildmodellen, welche durch bestimmte stochastische partielle Differentialgleichungen (SPDE) beschrieben werden kiinnen. Das Problem wird speziell in seiner diskreten Form formuliert, und daran schlieBt sich eine ausfiihrliche analytische Behandlung des dazugehijrigen Minimierungsproblems an. Die Schwierigkeiten zur Realisierung und der rechnerische Aufwand geben zu erkennen, daB eine derartige Formulierung nicht zu Interpolationsalgorithmen von praktisthem Nutzen fiihren kann. Die Herleitung von praktisch verwertbaren Interpolationsalgorithmen baut auf eine alternative Formulierung des Optimierungsproblems auf. Diese neue Formulierung erlaubt eine Umwandlung des urspriinglichen Optimierungsproblems in ein solches ohne Nebenbedingungen. Es werden einige Interpolationsalgorithmen vorgeschlagen, welche auf verschiedene nichtkausale und semikausale SPDE-Bildmodelle aufbauen. SchlieDlich werden experimentelle Ergebnisse dargestellt, verglichen und diskutiert. R&I&. Nous prbsentons dans cet article une approche nouvelle pour l'interpolation d'images bash sur les principes du calcul des variations. Ce problZme d'interpolation d'images est form& comme la minimisation sous contrainte d'une certaine fonctionnelle. Le choix de la fonctionnelle $ minimiser est bast sur une combinaison de restrictions impo&es par le problbme mathtmatique et sur certaines consid&ations likes au probl&ne physique. Cet article est focalist sur une classe de fonctionnelles quadratiques non-ntgatives dont la minimisation implique des fonctions splines Lg&ntralis&es 2-D. Les op&rateurs de diffkrentiation partielle utilids correspondent $ certains modbles d'images bash sur une buation diff&ntielle partielle stochastique (SPDE). L'interpolation d'images est form&e de man&e exact sous sa forme disc&e et nous pr&entons ensuite le probl&ne de minimisation r&&ant. Les difficult&s d'implantation et les calculs nt5cessaires indiquent qu'une telle formulation ne pcut foumir des algorithmes d'interpolation de valeur pratique. La dCrivation d'algorithmes d'interpolation pratiques est bas& sur une formulation alternative du problkme d'optimalisation. Cette nouvelle formulation permet la transformation de la minimisation sous contraintes en une tquivalente sans contraintes. Nous proposons un certain nombre d'algorithmes bas& sur plusieurs modiles d'images SPDE causaux et semi causaux. Finalement nous prksentons, comparons et discutons des r&ultats exp&imentaux.

Rational filters are extended to multichannel signal processing and applied to image interpolation. Two commonly used decimation schemes are considered: a rectangular grid and a quincunx grid. For each decimation lattice, we propose a number of adaptive resampling algorithms based on the vector rational filter (VRF). These algorithms exhibit desirable properties such as edge and detail preservation and accurate chromaticity estimation. In these approaches, color image pixels are considered as three-component vectors in the color space. Therefore, the inherent correlation that exists between the different color components is not ignored. This leads to better image quality compared to that obtained by componentwise or marginal processing. Extensive simulations show that multichannel image processing with the proposed algorithms (VRF L ) and (VRF d ) based on l p -norm and directional processing, respectively; significantly outperform linear and some nonlinear techniques, e.g., vector FIR median hybrid filters (VFMH). Some images interpolated using VRF L and VRF d are presented for qualitative comparison. These images are free from blockiness and jaggedness, confirming the quantitative results.

A novel evaluation study of the most appropriate round function for nearest-neighbor (NN) image interpolation is presented. Evaluated rounding functions are selected among the five rounding rules defined by the Institute of Electrical and Electronics Engineers (IEEE) 754-2008 standard. Both full- and no-reference image quality assessment (IQA) metrics are used to study and evaluate the influence of rounding functions on NN interpolation image quality. The concept of achieved occurrences over targeted occurrences is used to determine the percentage of achieved occurrences based on the number of test images used. Inferential statistical analysis is applied to deduce from a small number of images and draw a conclusion of the behavior of each rounding function on a bigger number of images. Under the normal distribution and at the level of confidence equals to 95%, the maximum and minimum achievable occurrences by each evaluated rounding function are both provided based on the inferential analysis-based experiments.

In this paper, we propose the design and implementation of an interpolation scheme for performing image scaling by utilizing a dynamic mask combined with a sophisticated neighborhood averaging fuzzy algorithm. The functions that contribute to the final interpolated image are the areas of the input pixels, overlapped by a dynamic mask, and the difference in intensity between the input pixels. Fuzzy if-then rules for these two functions are presented to carry out the interpolation task. Simulation results have shown a fine high-frequency response and a low interpolation error, in comparison with other widely used algorithms. The interpolation can be applied to both gray-scale and color images for any scaling factor. The proposed hardware structure is implemented in a field-programmable gate array (FPGA) chip and is based on a sequence of pipeline stages and parallel processing to minimize computation times. The fuzzy image interpolation implementation combines a fuzzy inference system and an imageinterpolation technique in one hardware system. Its main features are the ability to accurately approximate the Gaussian membership functions used by the fuzzy inference system with very few memory requirements and its high-frequency performance of 65 MHz, making it appropriate for real-time imaging applications. The system can magnify gray-scale images of up to 10-bit resolution. The maximum input image size is 1024 × 1024 pixels for a maximum of 800% magnification.

Five different particle image velocimetry (PIV) interrogation algorithms are tested with numerically generated particle images and two real data sets measured in turbulent flows with relatively small particle images of size 1.0-2.5 pixels. The size distribution of the particle images is analyzed for both the synthetic and the real data in order to evaluate the tendency for peaklocking occurrence. First, the accuracy of the algorithms in terms of mean bias and rms error is compared to simulated data. Then, the algorithms' ability to handle the peak-locking effect in an accelerating flow through a 2:1 contraction is compared, and their ability to estimate the rms and Reynolds shear stress profiles in a near-wall region of a turbulent boundary layer (TBL) at Re s =510 is analyzed. The results of the latter case are compared to direct numerical simulation (DNS) data of a TBL. The algorithms are: standard fast Fourier transform cross-correlation (FFT-CC), direct normalized crosscorrelation (DNCC), iterative FFT-CC with discrete window shift (DWS), iterative FFT-CC with continuous window shift (CWS), and iterative FFT-CC CWS with image deformation (CWD). Gaussian three-point peak fitting for sub-pixel estimation is used in all the algorithms. According to the tests with the non-deformation algorithms, DNCC seems to give the best rms estimation by the wall, and the CWS methods give slightly smaller peak-locking observations than the other methods. With the CWS methods, a bias error compensation method for the bilinear image interpolation, based on the particle image size analysis, is developed and tested, giving the same performance as the image interpolation based on the cardinal function. With the CWD algorithms, the effect of the spatial filter size between the iteration loops is analyzed, and it is found to have a strong effect on the results. In the near-wall region, the turbulence intensity varies by up to 4%, depending on the chosen interrogation algorithm. In addition, the algorithms' computational performance is tested.

We consider the problem of image interpolation using adaptive optimal recovery. We adaptively estimate the local quadratic signal class of our image pixels. We then use optimal recovery to estimate the missing local samples based on this quadratic signal class. This approach tends preserve edges, interpolating along edges and not across them.

In this paper, a new wavelet-based image interpolation algorithm is developed for magnifying the image details so that the visibility of tiny features in a digital image is improved. The algorithm takes the LR image as the low-pass filtered subband of an unknown wavelet transformed high resolution image. Then an initial HR image of size twice the LR image is estimated using zero padding of the details. The HR image is transformed via UWT resulting in four subbands, three of which are related with the high frequency components of the image. In the UWT domain, the LL subband is replaced with the initially estimated HR image and applying the inverse UWT, the final HR image is determined. Experiments conducted with both gray level and color images show the superiority of the proposed algorithm over the state-of-the-art interpolation methods.

Using texture and depth maps of a single reference viewpoint, depthimage-based rendering (DIBR) can synthesize a novel viewpoint image by translating texture pixels of the reference view to a virtual view, where synthesized pixel locations are derived from the associated depth pixel values. When the virtual viewpoint is located much closer to the 3D scene than the reference view (camera movement in the z-dimension), objects closer to the camera will increase in size in the virtual view faster than objects further away. A large increase in object size means that a patch of pixels sampled from an object surface in the reference view will be scattered to a larger spatial area, resulting in expansion holes. In this paper, we investigate the problem of identification and filling of expansion holes. We first propose a method based on depth histogram to identify missing or erroneously translated pixels as expansion holes. We then propose two techniques to fill in expansion holes with different computation complexity: i) linear interpolation, and ii) graph-based interpolation with a sparsity prior. Experimental results show that proper identification and filling of expansion holes can dramatically outperform inpainting procedure employed in VSRS 3.5 (up to 4.25dB).

A new scheme is presented for determining the range and orientation of arbitrarily oriented surfaces directly from the apparent image motion captured by a moving camera. The mean range, slope, and tilt of the surface are computed by decomposition of the image motion into components representing translation, compression, and shear. These motion parameters are computed directly from intensity changes in the images by the solution of a set of linear equations, the coefficients of which are derived from the raw image data by a novel process of linear image interpolation. The advantages of this scheme over those of existing schemes are that (1) it involves no identification or tracking of features and therefore avoids the correspondence problem, (2) it does not require measurement of high-order spatial or temporal derivatives of the image and is therefore robust to noise, and (3) the three parameters characterizing the surface (mean range, slope, and tilt) are recovered in a noniterative calculation. The scheme is ideal for applications such as autonomous robot navigation in which information about slope and tilt can be used to determine the traversability of the surrounding terrain.

Background: Gibbs ringing has been shown as a possible source of dark rim artifacts in myocardial perfusion studies. This type of artifact is usually described as transient, lasting a few heart beats, and localised in random segments of the myocardial wall. Dark rim artifacts are known to be unpredictably variable. This article aims to illustrate that a sub-pixel shift, i.e. a small displacement of the pixels with respect to the endocardial border, can result in different Gibbs ringing and hence different artifacts. Therefore a hypothesis for one cause of dark rim artifact variability is given based on the sub-pixel position of the endocardial border. This article also demonstrates the consequences for Gibbs artifacts when two different methods of image interpolation are applied (post-FFT interpolation, and pre-FFT zero-filling).

Motion estimation methods based on differential techniques proved to be very useful in the context of video analysis, but have a limited employment in classical video compression because, though accurate, the dense motion vector field they produce requires too much coding resource and computational effort. On the contrary, this kind of algorithm could be useful in the framework of distributed video coding (DVC). In this paper we propose a differential motion estimation algorithm which can run at the decoder in a DVC scheme, without requiring any increase in coding rate. This algorithm allows a performance improvement in image interpolation with respect to state-of-the-art algorithms.

A novel three-dimensional gray-level interpolation method called the Directional Coherence Interpolation (DCI) is presented in the paper. The principal advantage of the proposed approach is that it leads to significantly higher visual quality in 3D rendering when compared with traditional image interpolation methods. The basis of DCI is a form of directional image-space coherence. DCI interpolates the missing image data along the maximum coherence directions (MCD), which are estimated from the local image intensity yet constrained by a generic smoothness term. In order to further improve both the algorithm efficiency and robustness, we also propose to apply a pyramidal search strategy for MCD estimation. This coarse-to-fine scheme requires less computation time by starting with the reduced amount of data and propagating searching results to finer resolutions. DCI can incorporate image shape and structure information without the prior requirement of explicit representation of object ...

In this paper we propose the use of wavelets to accelerate the solution of the System of Linear Algebraic Equations that arise from the formulation of the problem of image interpolation from scattered data by means of Compactly-Supported Radial Basis Functions. Examples demonstrate the superiority of the solution in the wavelet domain using preconditioned iterative Krylov methods.

When magnifying a bitmapped image, we want to increase the number of pixels it covers, allowing for finer details in the image, which are not visible in the original image. Simple interpolation techniques are not suitable because they introduce jagged edges, also called "jaggies". Earlier we proposed the "mmint" magnification method (for integer scaling factors), which avoids jaggies. It is based on mathematical morphology. The algorithm detects jaggies in magnified binary images (using pixel replication) and removes them, making the edges smoother. This is done by replacing the value of specific pixels. In this paper, we extend the binary mmint to greyscale images. The pixels are locally binarized so that the same morphological techniques can be applied as for mmint. We take care of the more difficult replacement of pixel values, because several grey values can be part of a jaggy. We then discuss the visual results of the new greyscale method.

2D MRSI suffers from the effect of the spatial response function due to the truncation of the sampling of k-space. Filtering of the k-space data-set is often used to suppress the side lobes caused by the effects of the SRF, where the sampled data-set is multiplied with a weighting function before inverse FT. Commonly used filters in MRSI are the cosine, Hanning and Hamming filters. The data-set is often interpolated into a larger image matrix size for analysis, where "Fourier interpolation" (FoI) and "cubic spline interpolation" (CSpI) are two common methods. In this work, the effects of k-space filtering in practical usage was examined, and the image representations of the object for the two interpolation methods were compared.

The growth in practical applications for iris biometrics has been accompanied by relevant developments in the underlying algorithms and techniques. Along with the research focused on near-infrared (NIR) cooperatively captured images, efforts are being made to minimize the trade-off between the quality of the captured data and the recognition accuracy on less constrained environments, where images are obtained at the

The motion of an imaging device relative to the environment can, theoretically, be determined from the spatiotemporal intensity changes induced on the image plane of the device. We present a noniterative method for computing the six parameters of egomotion (three translatory and three rotational) from this visual input. The scheme is initially tested in a ray-traced environment to show proof of concept and to explore factors that influence its performance. We then demonstrate its performance on a multilobed camera, which is moved by arbitrary amounts in space. We also discuss and describe some practical implementations.

In this paper, a linear minimum mean square error (LMMSE) solution to the image interpolation problem is presented. The image interpolation problem is treated as an inverse problem considering the mathematical model by which a low resolution image is obtained from a high resolution image. In the suggested LMMSE (optimum) image interpolation algorithm, three main problems are encountered and solved. The autocorrelation matrix of the high resolution image is required prior to the interpolation process. This matrix is approximated from a polynomial based interpolated image and the sensitivity of the LMMSE solution to the estimation of the autocorrelation matrix is studied. Another problem is the noise variance estimation of the low resolution image. The sensitivity of the LMMSE solution to the noise variance estimation is also studied. The third problem is the large dimension matrix inversion process required for evaluating the high resolution image. This problem is solved by approximating the matrix to be inverted by a sparse matrix. Results show that the suggested solution is superior to polynomial based image interpolation algorithms from the Mean Square Error (MSE) point of view. It is also efficient from the computational complexity point of view .