Conventional Interpolation Algorithms Research Articles

Dynamic Analysis Techniques for Road Bridges Under Large Traffic Flows

This work focuses on the dynamic analysis of road bridges under large microsimulated traffic flows, contributing an efficient dynamic analysis framework for future fatigue, comfort or driving safety studies in which realistic interactions between vehicles are considered. Different strategies to define the loading vector in the governing dynamic equations are presented for bridges discretized with beam and shell elements. The proposed interpolation techniques for beam-like models include a recursive binary search and a novel vectorized strategy based on shape functions expanded to the entire length of the deck. These are applied to a short simply supported bridge and a very long cable-stayed bridge under traffic flows generated with a cellular automata microsimulation model. The strategies presented here reduce the computational time required in conventional load interpolation algorithms, particularly in long bridges subject to dense traffic flows. Finally, the binary search algorithm is applied to the study of bridge decks discretized with shell elements under microsimulated traffic, demonstrating that high vehicle densities increase the contribution of the transverse vibration of the slab and the potential discomfort of pedestrians.

Research on simultaneous switch and multiple switch processing methods

In the simulation of complex power electronic equipment containing multiple power electronic switches, it is often encountered that the same switch occurs several times in one step, or there are multiple switch actions in one step, that is, multiple switches. When the switching event occurs between two moments, because the voltage value can only be updated at the end of the simulation step, there is a cumulative error in the current obtained by the integration. In traditional electromagnetic transient simulation, the interpolation algorithm and its deformation are commonly used to solve this problem. However, due to the complexity of the conventional interpolation algorithm, it will seriously affect the efficiency in large-scale power grid simulation, and can only be applied to offline simulation, which cannot meet the needs of real-time simulation. In this paper, a method is presented to deal with synchronous switches and multiple switches, which can reliably solve the problem of synchronous switches and multiple switches encountered in real-time simulation of complex power electronic systems.

Dynamic learnable degradation for blind super-resolution

Blind super-resolution endeavors to restore clear and high-resolution images from obscure degraded images. To comprehensively encompass the diverse range of real image degradations, prevailing research methods either explicitly or implicitly simulate image degradation. Nevertheless, a majority of prior approaches employ conventional interpolation algorithms for resize operations, thereby impeding the breadth of image degradation diversity. In the case of implicit models, we propose a dynamic learnable degradation (DLD) model, achieved through self-supervised training. This model leverages a dynamic downsampling module to generate a multitude of low-resolution images. By introducing DLD as a novel downsampling method, we devise an original explicit degradation model, which generates additional sets of training image pairs to enhance the practical restoration outcomes of ESRGAN for natural images, called Dynamic-ESRGAN. We also account for the overall degradation and put forth a hybrid-order degradation model to encompass the manifold degradations prevalent in natural images. Exhaustive experimental comparisons conducted on both synthetic and real datasets have unequivocally demonstrated the significant advantages of our DLD and Dynamic-ESRGAN models, both in terms of objective indicators and subjective perception.

Contour interpolation by deep learning approach.

Contour interpolation is an important tool for expediting manual segmentation of anatomical structures. The process allows users to manually contour on discontinuous slices and then automatically fill in the gaps, therefore saving time and efforts. The most used conventional shape-based interpolation (SBI) algorithm, which operates on shape information, often performs suboptimally near the superior and inferior borders of organs and for the gastrointestinal structures. In this study, we present a generic deep learning solution to improve the robustness and accuracy for contour interpolation, especially for these historically difficult cases. A generic deep contour interpolation model was developed and trained using 16,796 publicly available cases from 5 different data libraries, covering 15 organs. The network inputs were a image patch and the two-dimensional contour masks for the top and bottom slices of the patch. The outputs were the organ masks for the three middle slices. The performance was evaluated on both dice scores and distance-to-agreement (DTA) values. The deep contour interpolation model achieved a dice score of and a mean DTA value of , averaged on 3167 testing cases of all 15 organs. In a comparison, the results by the conventional SBI method were and , respectively. For the difficult cases, the dice score and DTA value were and by the deep interpolator, compared with and by SBI. The t-test results confirmed that the performance improvements were statistically significant ( ) for all cases in dice scores and for small organs and difficult cases in DTA values. Ablation studies were also performed. A deep learning method was developed to enhance the process of contour interpolation. It could be useful for expediting the tasks of manual segmentation of organs and structures in the medical images.

STDIN: Spatio-temporal distilled interpolation for electron microscope images

Recently, flow-based approaches have shown considerable success in interpolating video images. However, in contrast to video images, electron microscope (EM) images are further complex due to noise and severe deformation between consecutive sections. Consequently, conventional flow-based interpolation algorithms, which assume a single offset per position, are not able to robustly model the movement of such complicated data. To address the aforementioned problems, this study propose a novel EM image interpolation framework that accommodates a range of offsets per location and further distills the intermediate features. First, a spatio-temporal ensemble (STE) interpolation module for capturing the missing middle features is presented. The STE is subdivided into two modules: temporal interpolation and residual spatial-correlated block (RSCB). The former predicts the intermediate features in two directions with several offsets at each location. Moreover, the RSCB uses the correlation coefficients for aggregated sampling. Thus, even if intermediate features are severely deformed, the STE effectively improves their accuracy. Second, a stackable feedback distillation block (SFDB) is introduced, which enhances the quality of intermediate features by distilling them from the input, and interpolated images, using a feedback mechanism. Extensive experiments demonstrate that the proposed method presents a superior performance compared with previous studies, both quantitatively and qualitatively.

A Measurement Method for Wide-frequency Harmonic Signal in Power Grid Based on Window Interpolation FFT and RLS-Adaline Neural Network

With the increase of non-linear loads in power grid and the development of power electronics, the growing serious harmonic pollution has put forward higher requirements for the accuracy and real-time performance of the measurement method. For this problem, this paper proposes a wide-frequency harmonic signal measurement method based on window interpolation FFT and adaptive neural network. After the window function is selected, the frequency of harmonic is estimated by double-spectrum-line interpolation algorithm based on three-term third derivative Nuttall window, and then the smoothed frequencies are input into RLS-Adaline neural network to estimate the amplitude and phase of each harmonic. The simulation results show that the proposed method has higher measurement accuracy and stability under few data conditions and low signal-to-noise ratios than conventional window interpolation algorithms. High accuracy measurement of harmonics with short time window in the range of 0∼2.5kHz is effectively realized.

Edge‐guided with gradient‐assisted depth up‐sampling

Most of depth up-sampling algorithms are based on the consistent hypothesis, i.e. the object boundaries in the colour image are consistent with depth discontinuity regions in the depth map. However, the hypothesis is not always correct. Under the combined guidance of high-resolution (HR) depth edge map and HR colour image gradient map, a simple and efficient depth up-sampler is presented. Firstly, the consistent regions are distinguished from the other regions and more accurate depth edge points are found. Then, the initial up-sampled depth map from traditional bilinear interpolation is refined by an effective depth-assignment scheme. Extensive experiments demonstrate that the proposed method outperforms conventional interpolation algorithms and some other edge-based depth up-sampling methods.

Super-resolution algorithm based on sparse representation and wavelet preprocessing for remote sensing imagery

An effective super-resolution (SR) algorithm is proposed for actual spectral remote sensing images based on sparse representation and wavelet preprocessing. The proposed SR algorithm mainly consists of dictionary training and image reconstruction. Wavelet preprocessing is used to establish four subbands, i.e., low frequency, horizontal, vertical, and diagonal high frequency, for an input image. As compared to the traditional approaches involving the direct training of image patches, the proposed approach focuses on the training of features derived from these four subbands. The proposed algorithm is verified using different spectral remote sensing images, e.g., moderate-resolution imaging spectroradiometer (MODIS) images with different bands, and the latest Chinese Jilin-1 satellite images with high spatial resolution. According to the visual experimental results obtained from the MODIS remote sensing data, the SR images using the proposed SR algorithm are superior to those using a conventional bicubic interpolation algorithm or traditional SR algorithms without preprocessing. Fusion algorithms, e.g., standard intensity-hue-saturation, principal component analysis, wavelet transform, and the proposed SR algorithms are utilized to merge the multispectral and panchromatic images acquired by the Jilin-1 satellite. The effectiveness of the proposed SR algorithm is assessed by parameters such as peak signal-to-noise ratio, structural similarity index, correlation coefficient, root-mean-square error, relative dimensionless global error in synthesis, relative average spectral error, spectral angle mapper, and the quality index Q4, and its performance is better than that of the standard image fusion algorithms.

The Geneva Reduction and Analysis Pipeline for High-contrast Imaging of planetary Companions

We present GRAPHIC, a new angular differential imaging (ADI) reduction pipeline where all geometric image operations are based on Fourier transforms. To achieve this goal the entire pipeline is parallelised making it possible to reduce large amounts of observation data without the need to bin the data. The specific rotation and shift algorithms based on Fourier transforms are described and performance comparison with conventional interpolation algorithm are given. Tests using fake companions injected in real science frames demonstrate the significant gain obtained by using geometric operations based on Fourier transforms compared to conventional interpolation. This also translates in a better point spread function and speckle subtraction with respect to conventional reduction pipelines, achieving detection limits comparable to current best performing pipelines. Flux conservation of the companions is also demonstrated. This pipeline is currently able to reduce science data produced by VLT/NACO, Gemini/NICI, VLT/SPHERE, and Subaru/SCExAO.

Fuzzy Based Distance Correction Algorithm for Digital Image Interpolation

Image interpolation is one of the key technologies in image processing. A special distance correction algorithm based on membership function is proposed in this paper to complete image interpolation. Fuzzy logic is used to get the membership function with the local characteristics of the gradient and phase angle. The first step is to correct the special distance of interpolated pixels along one dimension in the basis of local asymmetry features and the membership function, then convert the corrected distance of one dimension into two dimension, applying the corrected distance to conventional image interpolation algorithms. Experimental results demonstrate that this algorithm can produce better results in regard to the signal-to-nosie ratio and succeeds in preserving interpolation image edges of various directions.

이산 웨이블릿 변환을 이용한 영상의 초고해상도 기법

In this paper, we propose a super-resolution algorithm using discrete wavelet transform. In general super-resolution algorithms for single-image, probability based operations have been used for searching high-frequency components. Consequently, the complexity of the algorithm causes the increase of processing time. In the proposed algorithm, we use discrete wavelet transform to find high-frequency sub-bands. We perform inverse discrete wavelet transform using input image and high-frequency sub-bands of the same resolution as the input image which are obtained by performing discrete wavelet transform without down-sampling and then we obtain image with high-resolution. In the proposed algorithm, we use the down-sampled version of the original image () as a test image () to compare the performance of algorithms. Through experimental results, we confirm the improved efficiency of the proposed algorithm comparing with conventional interpolation algorithms and also decreased processing time comparing the probability based operations.

Nearest Neighbor Value Interpolation

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.

Real-Time B-Spline Interpolator with Look-Ahead Scheme for High-Speed CNC Machine Tools

NC tool paths of digital CAD models are currently generated as a set of discrete data points. The CNC interpolator must convert these points into continuous machine tool axis motions. In order to achieve high-speed and high-accuracy machining, the development of a real-time interpolation algorithm is really indispensable, which can deal with a large number of short blocks and still maintain smooth interpolation with an optimal speed. In this paper, a real-time local cubic B-spline interpolator with look-ahead scheme is proposed for consecutive micro-line blocks interpolation. First, the consecutive micro-line blocks that satisfy the bi-chord error constraints are fitted into a C1 continuous cubic B-spline curve. Second, machining dynamics and tool path contour constrains are taken into consideration. Third, local cubic B-spline interpolator with an optimal look-ahead scheme is proposed to generate the optimal speed profile. Simulation and experiment are performed in real-time environment to verify the effectiveness of the proposed method. Compared with the conventional interpolation algorithm, the proposed algorithm reduces the machining time by 70%.

Adaptive image interpolation algorithm based on the fuzzy logic

Conventional image interpolation algorithm always introduces the the blur and jagged edges. To solve this problem, an improved adaptive image interpolation algorithm with using membership function is proposed in this paper. Fuzzy logic is used to obtain the membership function with the local characteristics of the gradient and phase angle. The first step is to correct the special distance of interpolated pixels along one dimension in the basis of local asymmetry features and the membership function, and then to convert the corrected distance of one dimension into two dimensions, applying the corrected distance to conventional image interpolation algorithm. Experimental results demonstrate that the improved algorithm can produce better results in regard to the signal-to-nosie ratio and succeed in preserving interpolation image edges in various directions.

A CNC Interpolation Algorithm for Parametric Curves Based on Feedback Compensation

In order to maintain a high machining accuracy and a constant speed feedrate in CNC machining and to improve the capability of CNC machining in handling complex parts, the complicated interpolation algorithms need to be used in CNC interpolator, which is time-consuming for large amount of computation, thus influencing the machining speed. To solve this problem, a simple and efficient CNC interpolation algorithm based on feedback interpolation action is presented, which corrects repeatedly the interpolation errors to meet the interpolation requirements. The simulation results demonstrate the proposed algorithm can satisfy the machining of a variety of different parametric curves. Compared with the conventional interpolation algorithm, it features high universality, small computational amount small feedrate error and high computational accuracy, thereby greatly shortening the processing time and improving the processing efficiency. The feedback interpolation compensation algorithm implements on PMC32-600 DSP motion control card. The real cutting experiment proves that the algorithm can effectively guarantee the constancy of feedrate and the smoothness of parametric curves, thus improving the processing quality.

A new adaptive interpolation algorithm for 3D ultrasound imaging with speckle reduction and edge preservation

Conventional interpolation algorithms for reconstructing freehand three-dimensional (3D) ultrasound data always contain speckle noises and artifacts. This paper describes a new algorithm for reconstructing regular voxel arrays with reduced speckles and preserved edges. To study speckle statistics properties including mean and variance in sequential B-mode images in 3D space, experiments were conducted on an ultrasound resolution phantom and real human tissues. In the volume reconstruction, the homogeneity of the neighborhood for each voxel was evaluated according to the local variance/mean of neighboring pixels. If a voxel was locating in a homogeneous region, its neighboring pixels were averaged as the interpolation output. Otherwise, the size of the voxel neighborhood was contracted and the ratio was re-calculated. If its neighborhood was deemed as an inhomogeneous region, the voxel value was calculated using an adaptive Gaussian distance weighted method with respect to the local statistics. A novel method was proposed to reconstruct volume data set with economical usage of memory. Preliminary results obtained from the phantom and a subject's forearm demonstrated that the proposed algorithm was able to well suppress speckles and preserve edges in 3D images. We expect that this study can provide a useful imaging tool for clinical applications using 3D ultrasound.

Edge 방향의 비균등 데이터를 위한 개선된 Cubic Convolution Scaler

본 논문에서는 디지털 영상의 해상도를 임의의 배율로 확대 또는 축소하기 위해 사용되는 개선된 cubic convolution scaler를 제안한다. 화면 해상도 변경 시 엣지 부분에서 큰 왜곡이 발생되는 문제를 극복하기 위하여 제안하는 해상도 변환 방법은 영상의 edge의 방향에 따라 적용되며, 이것은 해상도 변환된 영상의 edge 특징을 잘 보존시킬 뿐 아니라 영상의 화질도 좋게 한다. 하지만 영상 보간에 사용되는 edge 방향 데이터들이 비균등 간격으로 위치하는 특징을 가지므로 cubic convolution의 kernel을 이에 맞게 새롭게 설계하였다. 제안하는 해상도 변환 방법은 transcoder와 같이 해상도 조정을 필요로 하면서 변환된 영상의 화질을 우수하게 유지하여야 하는 여러 응용분야에서 중요하게 사용된다. 실험 결과에서는 제안하는 방법으로 변환된 영상이 기존 보간 방법을 사용하여 변환된 영상에 비해 artifact를 가지지 않으면서도 좀 더 깨끗한 edge 정보를 가지고 있다는 것을 보여준다. 또 기존 방법에 비해 제안하는 방법은 해상도 변환에 의한 정보의 손실을 최소화 하였다. We derive a modified version of the cubic convolution scaler to enlarge or reduce the size of digital image with arbitrary ratio. To enhance the edge information of the scaled image and to obtain a high-quality scaled image, the proposed scaler is applied along the direction of an edge. Since interpolation along the direction of an edge has to process nonuniformly sampled data, the kernel of the cubic convolution scaler is modified to interpolate the data. The proposed scaling scheme can be used to resize pictures in various formats in a transcoding system that transforms a bit stream compressed at one bit rate into one compressed at another bit rate. In many applications, such as transcoders, the resolution conversion is very important for changing the image size while maintaining high quality of the scaled image. We show experimental results that demonstrate the effectiveness of the proposed interpolation method. The proposed scheme provides clearer edges, without artifacts, in the resized image than do conventional schemes. The algorithm exhibits significant improvement in the minimization of information loss when compared with the conventional interpolation algorithms.

Modified cubic convolution scaler for edge-directed nonuniform data

We derive a modified version of the cubic convolution scaler to enlarge or reduce the size of digital images with arbitrary ratio. To enhance the edge information of the scaled image and to obtain a high-quality scaled image, the proposed scaler is applied along the direction of an edge. Since interpolation along the direction of an edge has to process nonuniformly sampled data, the kernel of the cubic convolution scalar is modified to interpolate the data. The proposed scaling scheme can be used to resize pictures in various formats in a transcoding system that transforms a bit stream compressed at one bit rate into one compressed at another bit rate. In many applications, such as transcoders, the resolution conversion is very important for changing the image size while maintaining high quality of the scaled image. We show experimental results that demonstrate the effectiveness of the proposed interpolation method. The proposed scheme provides clearer edges, without artifacts, in the resized image than do conventional schemes. The algorithm exhibits significant improvement in the minimization of information loss when compared with the conventional interpolation algorithms.

Image interpolation using interpolative classified vector quantization

According to advances in digital imaging technology, interest in high-resolution (HR) images has been increased. Various methods that convert low-resolution (LR) images to HR ones have been presented. In this paper, to reduce the computational load we propose a vector quantization (VQ) based algorithm that reconstructs an interpolation image by adding to an initially interpolated image high-frequency components predicted from training with a number of example image sets. The proposed interpolative classified VQ (ICVQ) algorithm combines interpolative VQ with classified VQ. With a number of (LR and HR) example image sets, we construct two types of (LR and HR) codebooks. Comparative experiments with three conventional image interpolation algorithms show that the proposed interpolation algorithms using ICVQ effectively preserve edges to which the human visual system is sensitive. The proposed algorithm can be applicable to various image- and video-based applications such as digital camera and digital television.

A fast edge-oriented algorithm for image interpolation

This paper introduces a fast algorithm for image interpolation. By using this method, real-time enlargement of video images is accessible. The basic idea of the algorithm is to partition digital images into homogeneous and edge areas based on the analysis of the local structure on the images. In addition, in order to have better performance on interpolating images, specified algorithms are assigned to interpolate each classified areas, respectively. Experimental results show that the subjective quality of the interpolated images is substantially improved by using the proposed algorithm compared with that of using conventional interpolation algorithms. The computational complexity of the proposed algorithm is much lower than those of others mentioned in this paper. We also successfully implemented real-time enlargement of QCIF video sequences to CIF sequences with better image quality in low bit-rate environment.