Showing papers on "Bicubic interpolation published in 2015"

Harmonic Estimation Using Symmetrical Interpolation FFT Based on Triangular Self-Convolution Window

Abstract: Harmonic estimation is an important topic in power system signal processing. Windowed interpolation fast Fourier transformation (WIFFT) is an efficient algorithm for power system harmonic estimation, which can eliminate the errors caused by spectral leakage and picket fence effect. However, the fitting polynomial in the interpolation procedure contains both even and odd terms, and this increases the computational burden. This paper proposes a simple symmetrical interpolation FFT algorithm, where the even terms are removed from the fitting polynomial based on the triangular self-convolution windows (TSCW). The polynomials for frequency and amplitude computations are provided. Considerable leakage errors and harmonic interferences can be suppressed by the TSCW. Accurate estimations of harmonic parameters can be obtained via the fitting polynomial and the TSCW, both with adjustable order to fulfill different accuracy and speed requirements of practical power harmonic measurement. Simulation results and measurements have validated the proposed method.

Fast Image Interpolation via Random Forests

Abstract: This paper proposes a two-stage framework for fast image interpolation via random forests (FIRF). The proposed FIRF method gives high accuracy, as well as requires low computation. The underlying idea of this proposed work is to apply random forests to classify the natural image patch space into numerous subspaces and learn a linear regression model for each subspace to map the low-resolution image patch to high-resolution image patch. The FIRF framework consists of two stages. Stage 1 of the framework removes most of the ringing and aliasing artifacts in the initial bicubic interpolated image, while Stage 2 further refines the Stage 1 interpolated image. By varying the number of decision trees in the random forests and the number of stages applied, the proposed FIRF method can realize computationally scalable image interpolation. Extensive experimental results show that the proposed FIRF(3, 2) method achieves more than 0.3 dB improvement in peak signal-to-noise ratio over the state-of-the-art nonlocal autoregressive modeling (NARM) method. Moreover, the proposed FIRF(1, 1) obtains similar or better results as NARM while only takes its 0.3% computational time.

Fourier-based interpolation bias prediction in digital image correlation.

Abstract: Based on the Fourier method, this paper deduces analytic formulae for interpolation bias in digital image correlation, explains the well-known sinusoidal-shaped curves of interpolation bias, and introduces the concept of interpolation bias kernel, which characterizes the frequency response of the interpolation bias and thus provides a measure of the subset matching quality of the interpolation algorithm. The interpolation bias kernel attributes the interpolation bias to aliasing effect of interpolation and indicates that high-frequency components are the major source of interpolation bias. Based on our theoretical results, a simple and effective interpolation bias prediction approach, which exploits the speckle spectrum and the interpolation transfer function, is proposed. Significant acceleration is attained, the effect of subset size is analyzed, and both numerical simulations and experimental results are found to agree with theoretical predictions. During the experiment, a novel experimental translation technique was developed that implements subpixel translation of a captured image through integer pixel translation on a computer screen. Owing to this remarkable technique, the influences of mechanical error and out-of-plane motion are eliminated, and complete interpolation bias curves as accurate as 0.01 pixel are attained by subpixel translation experiments.

Design of trigonometric velocity scheduling algorithm based on pre-interpolation and look-ahead interpolation

Abstract: To generate continuous velocity, acceleration and jerk curves of parametric interpolation in high-speed and high-accuracy machining, this paper presents a trigonometric velocity scheduling algorithm based on two-time look-ahead interpolation (pre-interpolation and look-ahead interpolation). The algorithm consists of three modules: pre-interpolation, look-ahead interpolation and real-time interpolation. The pre-interpolation module aims to explore and record the information of the path to be machined. The look-ahead interpolation module firstly calculates the velocity scheduling functions according to the data recorded by pre-interpolation, and then tests and adjusts the feedrate scheduling schemes constantly. The real-time interpolation module adapts the method of beforehand deceleration or delaying acceleration according to the signals received from pre-interpolation and look-ahead interpolation to guarantee the processing accuracy. Simulation and experimental tests demonstrate the availability, effectiveness and advantages of the trigonometric velocity scheduling algorithm. And the proposed trigonometric velocity scheduling algorithm based on pre-interpolation and look-ahead interpolation could realize smooth velocity, acceleration and jerk control with restricted chord error and implement high-quality CNC processing.

Fast Subpixel Mapping Algorithms for Subpixel Resolution Change Detection

Abstract: Due to rapid changes on the Earth's surface, it is important to perform land cover change detection (CD) at a fine spatial and fine temporal resolution. However, remote sensing images with both fine spatial and temporal resolutions are commonly not available or, where available, may be expensive to obtain. This paper attempts to achieve fine spatial and temporal resolution land cover CD with a new computer technology based on subpixel mapping (SPM): The fine spatial resolution land cover maps (FRMs) are first predicted through SPM of the coarse spatial but fine temporal resolution images, and then, subpixel resolution CD is performed by comparison of class labels in the SPM results. For the first time, five fast SPM algorithms, including bilinear interpolation, bicubic interpolation, subpixel/pixel spatial attraction model, Kriging, and radial basis function interpolation methods, are proposed for subpixel resolution CD. The auxiliary information from the known FRM on one date is incorporated in SPM of coarse images on other dates to increase the CD accuracy. Based on the five fast SPM algorithms and the availability of the FRM, subpixels for each class are predicted by comparison of the estimated soft class values at the target fine spatial resolution and borrowing information from the FRM. Experiments demonstrate the feasibility of the five SPM algorithms using FRM in subpixel resolution CD. They are fast methods to achieve subpixel resolution CD.

Multidirectional Weighted Interpolation and Refinement Method for Bayer Pattern CFA Demosaicking

Abstract: This paper presents a novel multidirectional weighted interpolation algorithm for color filter array interpolation. Our proposed method has two contributions to demosaicking. First, different from conventional interpolation methods based on two directions or four directions, the proposed method exploits to greater degree correlations among neighboring pixels along eight directions to improve the interpolation performance. Second, we propose an efficient postprocessing method to reduce interpolation artifacts based on the color difference planes. Compared with conventional state-of-the-art demosaicking algorithms, our experimental results show the proposed algorithm provides superior performance in both objective and subjective image quality. Furthermore, this implementation has moderate computational complexity.

Multidirectional Weighted Interpolation and Refinement Method for Bayer Pattern

Abstract: This paper presents a novel multidirectional weighted interpolation algorithm for color filter array inter- polation. Our proposed method has two contributions to demosaicking. First, different from conventional interpolation methods based on two directions or four directions, the proposed method exploits to greater degree correlations among neighboring pixels along eight directions to improve the interpolation perfor- mance. Second, we propose an efficient postprocessing method to reduce interpolation artifacts based on the color difference planes. Compared with conventional state-of-the-art demosaick- ing algorithms, our experimental results show the proposed algorithm provides superior performance in both objective and subjective image quality. Furthermore, this implementation has moderate computational complexity.

ICDAR2015 competition on Text Image Super-Resolution

Abstract: This paper presents the first international competition on Text Image Super-Resolution (SR) and the ICDAR2015-TextSR dataset. We describe the core of the competition: interest, dataset generation and evaluation procedure, together with participating teams and their respective methods. The obtained results, along with baseline image upscaling schemes and state-of-the-art SR approaches are reported and commented. The main conclusion of this competition is that SR systems may improve OCR performances by up to 16.55 points in accuracy compared with bicubic interpolation for the proposed low resolution images.

Deep learning based super-resolution for improved action recognition

Abstract: Action recognition systems mostly work with videos of proper quality and resolution. Even most challenging benchmark databases for action recognition, hardly include videos of low-resolution from, e.g., surveillance cameras. In videos recorded by such cameras, due to the distance between people and cameras, people are pictured very small and hence challenge action recognition algorithms. Simple upsampling methods, like bicubic interpolation, cannot retrieve all the detailed information that can help the recognition. To deal with this problem, in this paper we combine results of bicubic interpolation with results of a state-of-the-art deep learning-based super-resolution algorithm, through an alpha-blending approach. The experimental results obtained on down-sampled version of a large subset of Hoolywood2 benchmark database show the importance of the proposed system in increasing the recognition rate of a state-of-the-art action recognition system for handling low-resolution videos.

Adaptive General Scale Interpolation Based on Weighted Autoregressive Models

Abstract: The autoregressive (AR) model has been widely used in signal processing for its effective estimation, especially in image processing. Many dedicated $2\times $ interpolation algorithms adopt the AR model to describe the strong correlation between low-resolution (LR) pixels and high-resolution (HR) pixels. However, these AR model-based methods closely depend on the fixed relative position between LR pixels and HR pixels that are nonexistent in the general scale interpolation. In this paper, we present an adaptive general scale interpolation algorithm that is capable of arbitrary scaling factors considering the nonstationarity of natural images. Different from other dedicated $2\times $ interpolation methods, the proposed AR terms are modeled by pixels with their adjacent unknown HR neighbors. To compensate for the information loss caused by mismatches of AR models, we consider a weighting scheme suitable for general scale situations based on the pixel similarity to increase accuracy of the estimation. Comprehensive experiments demonstrate the effectiveness of the proposed method on general scaling factors. The maximum gain of peak signal-to-noise ratio is 2.07 dB compared with segment adaptive gradient angle in $1.5 \times $ enlargements. To evaluate the performance in resolution adaptive video coding, we have also tested our method on Joint Scalable Video Model codec and obtained better subjective quality and rate-distortion performance.

Eulerian models and algorithms for unbalanced optimal transport

Abstract: Benamou and Brenier formulation of Monge transportation problem (Numer. Math. 84:375-393, 2000) has proven to be of great interest in image processing to compute warpings and distances between pair of images (SIAM J. Math. Analysis, 35:61-97, 2003). One requirement for the algorithm to work is to interpolate densities of same mass. In most applications to image interpolation, this is a serious limitation. Existing approaches to overcome this caveat are reviewed, and discussed. Due to the mix between transport and $L^2$ interpolation, these models can produce instantaneous motion at finite range. In this paper we propose new methods, parameter-free, for interpolating unbalanced densities. One of our motivations is the application to interpolation of growing tumor images.

New sub-pixel interpolation functions for accurate real-time stereo-matching algorithms

Abstract: Most of the stereo-matching algorithms nowadays need high accuracy, especially for objects at large distances. Lots of approaches are able to provide good results at low costs, but at large distances (small disparities) suffer from the so called “pixellocking effect” i.e. an uneven sub-pixel disparity distribution. In order to compensate this effect, developing sub-pixel interpolation functions can be a viable solution. Previous work has shown that each interpolation function should be adapted to each particular stereo-algorithm and the best way to obtain such a function is trough the process of function fitting. We propose several original interpolation functions for achieving highly accurate sub-pixel disparity maps when applied over the cost volume obtained with two high-speed Semi-Global Matching (SGM) algorithms. In this context we define a multi-objective performance metric for function fitting. To this end we have implemented a tool that provides qualitative measurements of the fitted function by the use of generated ground truth synthetic images, with sub-pixel precision. Real and synthetic test cases provide consistent data - the best interpolation functions for the SGM algorithms have been found and they outperform the state of the art functions, thus the proposed tool is validated.

Method for registering synthetic aperture radar image with change area based on point pair constraint and Delaunay

Abstract: The present invention discloses a method for registering synthetic aperture radar image with a change area based on a point pair constraint and Delaunay, and the problem of SAR image registration with a change area without using ground control point data is mainly solved. The method comprises a step of filtering inputted floating image and reference image respectively and then using a SURF algorithm to extract feature points, a step of carrying out similarity measure matching of a normalized descriptor matrix on obtained reference image feature point and floating image feature point and then carrying out distance and wiring direction angle constraint to obtain an initial candidate matching point pair set, a step of using matching feature points in the floating image and the reference image to construct Delaunay, searching a homonymous triangle pairs in the Delaunay of the two images to update the candidate matching point pair set, and taking non-repeated vertices in all homonymous triangle pairs corresponding to a minimum affine error as a final matching point pair set, and calculating an affine change matrix, carrying out affine conversion and bicubic interpolation on the floating image, and obtaining a final registration result image.

Smooth orientation path planning with quaternions using B-splines

Abstract: Many robotics applications require smooth orientation planning, i.e. interpolation or approximation of a frame orientation through prescribed configurations such that the angular velocity and its time derivatives are smooth. This for instance ensures the continuity of the motor torques of robotic manipulator. Yet no satisfactory solution to this problem has been presented. This paper presents a solution using a B-spline parameterization of rotations. The method allows for a continuous interpolation and approximation through a given set of quaternions up to a specified order. The algorithm resembles the well-known B-spline interpolation in the sense that it boils down to solving a system of linear equations. The method is demonstrated for a polishing application (car fender) with an industrial robot.

A new background subtraction method for energy dispersive X-ray fluorescence spectra using a cubic spline interpolation

Abstract: A new method is presented to subtract the background from the energy dispersive X-ray fluorescence (EDXRF) spectrum using a cubic spline interpolation. To accurately obtain interpolation nodes, a smooth fitting and a set of discriminant formulations were adopted. From these interpolation nodes, the background is estimated by a calculated cubic spline function. The method has been tested on spectra measured from a coin and an oil painting using a confocal MXRF setup. In addition, the method has been tested on an existing sample spectrum. The result confirms that the method can properly subtract the background.

Analyzing impact of image scaling algorithms on viola-jones face detection framework

Abstract: In today's world of automation, real time face detection with high performance is becoming necessary for a wide number of computer vision and image processing applications. Existing software based system for face detection uses the state of the art Viola and Jones face detection framework. This detector makes use of image scaling approach to detect faces of different dimensions and thus, performance of image scalar plays an important role in enhancing the accuracy of this detector. A low quality image scaling algorithm results in loss of features which directly affects the performance of the detector. Therefore, in this paper we have analyzed the effect of different image scaling algorithms existing in literature on the performance of the Viola and Jones face detection framework and have tried to find out the optimal algorithm significant in performance. The algorithms which will be analyzed are: Nearest Neighbor, Bilinear, Bicubic, Extended Linear and Piece-wise Extended Linear. All these algorithms have been integrated with the Viola and Jones face detection code available with OpenCV library and has been tested with different well know databases containing frontal faces.

Eigen-patch iris super-resolution for iris recognition improvement

Abstract: Low image resolution will be a predominant factor in iris recognition systems as they evolve towards more relaxed acquisition conditions. Here, we propose a super-resolution technique to enhance iris images based on Principal Component Analysis (PCA) Eigen-transformation of local image patches. Each patch is reconstructed separately, allowing better quality of enhanced images by preserving local information and reducing artifacts. We validate the system used a database of 1,872 near-infrared iris images. Results show the superiority of the presented approach over bilinear or bicubic interpolation, with the eigen-patch method being more resilient to image resolution reduction. We also perform recognition experiments with an iris matcher based 1D Log-Gabor, demonstrating that verification rates degrades more rapidly with bilinear or bicubic interpolation.

Unscented Transformation based array interpolation

Abstract: It is impossible to enforce exact responses for each sensor involved in an antenna array. Important signal processing techniques such as Estimation of Signal Parameters via Rotational Invariance (ESPRIT), Forward Backward Average (FBA) and Spatial Smoothing (SPS) rely on sensor arrays with Vandermonde or centro-hermitian responses. To achieve such responses array interpolation is often necessary. In this work a novel way of performing array interpolation while minimizing the transformation error using the Unscented Transformation (UT) is presented. The UT provides a different method for mapping interpolated regions and also exhibits a new insight into array interpolation and its current limitations. A set of numerical simulations presents promising results for array interpolation employing the UT.

Hybrid Gaussian-cubic radial basis functions for scattered data interpolation

Abstract: Scattered data interpolation schemes using kriging and radial basis functions (RBFs) have the advantage of being meshless and dimensional independent, however, for the data sets having insufficient observations, RBFs have the advantage over geostatistical methods as the latter requires variogram study and statistical expertise. Moreover, RBFs can be used for scattered data interpolation with very good convergence, which makes them desirable for shape function interpolation in meshless methods for numerical solution of partial differential equations. For interpolation of large data sets, however, RBFs in their usual form, lead to solving an ill-conditioned system of equations, for which, a small error in the data can cause a significantly large error in the interpolated solution. In order to reduce this limitation, we propose a hybrid kernel by using the conventional Gaussian and a shape parameter independent cubic kernel. Global particle swarm optimization method has been used to analyze the optimal values of the shape parameter as well as the weight coefficients controlling the Gaussian and the cubic part in the hybridization. Through a series of numerical tests, we demonstrate that such hybridization stabilizes the interpolation scheme by yielding a far superior implementation compared to those obtained by using only the Gaussian or cubic kernels. The proposed kernel maintains the accuracy and stability at small shape parameter as well as relatively large degrees of freedom, which exhibit its potential for scattered data interpolation and intrigues its application in global as well as local meshless methods for numerical solution of PDEs.

Interpolation of Discrete Time Signals Using Cubic Spline Function

Abstract: In this paper, a technique, which uses cubic spline, is proposed for the interpolation of discrete time signals and illustrated with examples. The results obtained are compared with the results of nearest neighbor interpolation and linear interpolation of discrete time signals. The analysis is made by calculating errors.

Image interpolation based on non-local geometric similarities

Abstract: Image interpolation refers to constructing a high-resolution (HR) image from a low-resolution (LR) image. Traditionally, an HR image can be produced from an observed LR image via the polynomial-based interpolation (bi-linear or bi-cubic interpolations, involving a small number of neighbors around each interpolated position). The advanced interpolation makes use of the so-called “geometric similarity” to design a set of optimal interpolation weighting coefficients. However, better geometric similarities can perhaps be found from a non-local area within the LR source image or even from other but similar images (possibly with higher resolutions). Based on this fact, we propose in this paper a non-local geometric similarity based interpolation scheme to construct HR images. In our proposed method, optimal weighting coefficients are determined by solving a regularized least squares problem which is built upon a number of dual reference patches drawn from the observed LR image and regularized by the variation of directional gradients of the image patch. Experimental results demonstrate that our proposed method offers a remarkable quality improvement, both objectively and subjectively.

Bilinear Interpolation over fuzzified images: Enlargement

Abstract: The paper explores Bilinear Interpolation applied to image enlargement after a fuzzification pre-processing. On the one hand, and from a theoretical point of view, we show some interesting relationships between Bilinear Interpolation and the Fuzzification. On the other hand, from an applied point of view we apply the interpolation obtained to enlargement and show that the obtained results are firstly, faster and secondly, comparable with the standard interpolation procedures.

Fast Measurement of Dielectric Loss Angle With Time-Domain Quasi-Synchronous Algorithm

Abstract: Fast Fourier transform (FFT) algorithm inevitably suffers from spectral leakage and picket-fence effect under asynchronous sampling and nonintegral period truncation. So the FFT-based dielectric loss angle (DLA) measurement needs window functions and spectral lines interpolation algorithm, which requires massive sampled data and high computational cost. Therefore, this paper proposes a DLA measurement method on the basis of time-domain quasi-synchronous sampling technique to achieve fast measurement and high accuracy. First, the fundamental frequencies of the original voltage and current sampled signals were estimated accurately with the time-domain Newton interpolation algorithm. Then, the quasi-synchronous sampled sequences (QSSA) of the original asynchronous sampled signals were reconstructed using cubic spline interpolation algorithm. Finally, the DLA was calculated according to the equivalent circuit model and frequency-domain analysis of voltage and current QSSA based on FFT. The proposed method in this paper can perfectly restrain spectral leakage and picket-fence effect without window functions and spectral lines interpolation algorithm. The results of simulation and the implementation on the embedded system have confirmed the effectiveness of the proposed algorithm in this paper with less sampled data and lower computational cost.

An analysis of interpolation methods for super resolution images

Abstract: The image such as CT scan, x - ray image, CCTV videos and hand phone's camera is kind of low resolution image producers. Digital camera captured the continuous scenes and transform into discrete presentation in term of space and intensity. In sampling process it may create aliasing and information lost at frequency below the Nyquist sampling rates. Therefore the image suffered with an ill-posed problem by aliasing and loss of frequency. The problem ill-pose problem could be solved by applying Super Resolution (SR) techniques. The SR process contains of image registration, interpolation and image reconstruction. However this paper is focus on an analysis the best performance offered by interpolation techniques. An analysis procedure requires interpolation kernel inspection into frequency domain plotting to determine the best kernel response in pass and stop band. Otherwise use Peak Signal to Noise Ratio as indicator the similarity simulated with original image. In this study found the cubic spline interpolation is provided the smoother function frequency response with less ripples in stop band and good pass response. Besides that, it shows a superiorly in lead the highest PSNR for all type image tests with several of upscale. The best response and less distortion effect generated by kernel is preferable candidate to produce an efficient image application with low maintenances.

Geomagnetic field-based localization with bicubic interpolation for mobile robots

Abstract: This paper proposes a novel geomagnetic field-based SLAM (simultaneous localization and mapping) technique for application to mobile robots. SLAM is an essential technique for mobile robots such as robotic vacuum cleaners to perform their missions autonomously. For practical application to commercialized robotic vacuum cleaners, the SLAM techniques should be implemented with low-priced sensors and low-computational complexity. Most building structures produce distortions in the geomagnetic field and variation of the field over time occurs with extremely low frequency. The geomagnetic field is hence applicable to mobile robot localization. The proposed geomagnetic field SLAM uses only the geomagnetic field signals and odometry data to estimate the robot state and the geomagnetic field signal distribution with low computational cost. To estimate the signal strength of the geomagnetic field, bicubic interpolation, an extension of cubic interpolation for interpolating surfaces on a regular grid, is used. The proposed approach yields excellent results in simulations and experiments in various indoor environments.

Towards a More General Type of Univariate Constrained Interpolation With Fractal Splines

Abstract: Recently, in [Electronic Transaction on Numerical Analysis, 41 (2014), pp. 420-442] authors introduced a new class of rational cubic fractal interpolation functions with linear denominators via fractal perturbation of traditional nonrecursive rational cubic splines and investigated their basic shape preserving properties. The main goal of the current article is to embark on univariate constrained fractal interpolation that is more general than what was considered so far. To this end, we propose some strategies for selecting the parameters of the rational fractal spline so that the interpolating curves lie strictly above or below a prescribed linear or a quadratic spline function. Approximation property of the proposed rational cubic fractal spine is broached by using the Peano kernel theorem as an interlude. The paper also provides an illustration of background theory, veined by examples.