Traditional Interpolation Algorithms Research Articles

A deep-learning super-resolution reconstruction model of turbulent reacting flow

Super-resolution reconstruction using deep-learning techniques is a valuable tool for enhancing computational and experimental visualization of flow fields to recover detailed features from coarse results. In this study, we propose the Pyramid Res U Super Resolution (PRUSR) network as a flow reconstruction model that upgrades the basic architecture of the Downsampled Skip-Connection Multi-Scale (DSC/MS) model with the pre-activated residual block (PRB). To improve the model accuracy, we also implement a new loss function called Mean Square Structure Similarity (MSSSIM), which combines the Mean Square Error (MSE) and Structure Similarity (SSIM). We test the model on a practical problem of visualizing the three-dimensional turbulent jet flame and find that the PRUSR model accurately reconstructs the super-resolution flow field, particularly in large downsampling ratio conditions, outperforming the original DSC/MS model and traditional interpolation algorithms such as bilinear and bicubic even with fewer input data. Additionally, the PRUSR model is capable of reconstructing sub-grid turbulent flow and flame thickness by inferring high-frequency information in the inertial subrange, making it a powerful post-processing tool for turbulent flow visualization and analysis.

A Super-Resolution Network for High-Resolution Reconstruction of Landslide Main Bodies in Remote Sensing Imagery Using Coordinated Attention Mechanisms and Deep Residual Blocks

The lack of high-resolution training sets for intelligent landslide recognition using high-resolution remote sensing images is a major challenge. To address this issue, this paper proposes a method for reconstructing low-resolution landslide remote sensing images based on a Super-Resolution Generative Adversarial Network (SRGAN) to fully utilize low-resolution images in the process of constructing high-resolution landslide training sets. First, this paper introduces a novel Enhanced Depth Residual Block called EDCA, which delivers stable performance compared to other models while only slightly increasing model parameters. Secondly, it incorporates coordinated attention and redesigns the feature extraction module of the network, thus boosting the learning ability of image features and the expression of high-frequency information. Finally, a residual stacking-based landslide remote sensing image reconstruction strategy was proposed using EDCA residual blocks. This strategy employs residual learning to enhance the reconstruction performance of landslide images and introduces LPIPS for evaluating the test images. The experiment was conducted using landslide data collected by drones in the field. The results show that compared with traditional interpolation algorithms and classic deep learning reconstruction algorithms, this approach performs better in terms of SSIM, PSNR, and LPIPS. Moreover, the network can effectively handle complex features in landslide scenes, which is beneficial for subsequent target recognition and disaster monitoring.

Reparametric Sampling for Small Object Image Recognition

It is difficult to balance detection speed and detection accuracy in current methods for sampling on feature maps. We combine the parameterless interpolation algorithm used in the traditional method with the algorithm in deep learning to improve shortcomings of the traditional interpolation algorithm by introducing learnable parameters into the algorithm. The new algorithm guarantees the features of the original image by calculating the correlation between feature map channels and pixels to ensure the correct restoration of the image features during up-sampling. In this paper, we propose a new up-sampling operation model — Reparametric Blend Up-sampling (RBU), which uses the method of rearranging pixels to obtain a feature map that matches the up-sampling size, uses blending to compute inter-pixel and inter-channel correlations and back-propagates the model to learn the appropriate parameters to ensure feature extraction. In the field of object detection, this model replaces the original up-sampling operation and improves the mAP mean value by 1.1% and the detection of small target objects by 3.1% in the VOC dataset with the richest application scenarios.

Consistent convolution kernel design for missing shots interpolation using an improved U‐net

ABSTRACTDuring seismic data acquisition, receivers with finer trace intervals can be distributed to record seismic signals. The distance between neighbouring shots, on the other hand, can be significant, resulting in regularly missing shots in the common receiver gathers, which may cause significant spatial aliasing and reduce the precision of later seismic imaging. Traditional interpolation algorithms have several issues and limitations for seismic data with spatial aliasing due to prior assumptions and human–computer interactions. As a result, we present a new deep learning method that includes the generation of adaptive training data and the design of a consistent convolution kernel. Deep learning has the ability to characterize seismic data in a nonlinear manner that is ideal for accurate seismic interpolation. Because the common shot gathers and common receiver gathers have similar features due to the spatial reciprocity of Green's function of the wave equation if all shots have the same physical signatures, the common shot gathers with a refined trace interval are adaptively extracted as the training dataset to guarantee the interpolation performance in the common receiver gathers. With regularly subsampled data as input an improved U‐Net is created and trained to match the desired output, that is, the completed data. The trained network can be deployed to the test common receiver gathers to rebuild regularly missing shots. We develop a novel consistent convolution kernel to ensure high accuracy of missing shot reconstruction while accounting for differences between prestack unmigrated seismic data and images. Using numerically subsampled synthetic and field data, the effectiveness and validity of the developed consistent convolution kernel and the upgraded U‐Net with adaptive training data for missing shot interpolation are demonstrated.

Fast Determination of Satellite-to-Moon Visibility Using an Adaptive Interpolation Method Based on Vertex Protection.

Fast determination of satellite visibility with respect to a target area is important for satellite navigation and positioning. In this paper, we propose an adaptive interpolation algorithm based on vertex protection to solve the satellite visibility period problem more accurately and quickly, where “vertex” refers to the local extremum point. The algorithm can avoid the error in the visibility period calculation caused by skimming the vertices when fitting the multi-hump visibility function under certain fitting accuracy requirements with the traditional adaptive interpolation method. The algorithm does not need to construct a cubic polynomial in each subinterval to determine whether the satellite is visible or not; it only constructs a cubic polynomial to solve the problem if the visibility function of that subinterval is judged to have a solution from the existence theorem of zero points, which can improve the computational efficiency. For the lunar navigation problem, a solution to satellite–Moon visibility calculations based on a vertex-protected adaptive interpolation is given, and the experimental results show that the computation time of the algorithm can be reduced by approximately 98% compared with the brute force method and by approximately 30% compared with the traditional adaptive interpolation algorithm.

Super-Resolution Reconstruction of Brain MR Images Using Pseudo-3D Convolutional Network

The medical image super-resolution (SR) reconstruction algorithm based on deep learning with standard 3D convolution has too many network parameters. This leads to high computational complexity and low network training efficiency. To address these problems, a lightweight densely-connected residual 3D-CNN (P3DSRNet) combining dense residual connection with pseudo-3D convolution is proposed. Firstly, the dense residual block is used to widen the channel of the convolution layer. Hence, more feature information is transmitted to the activation function, so that the low-layer image features are spread to the higher layers to improve medical image super-resolution. Then, the pseudo-3D separable convolution strategy is employed to train the network, where the standard 3D convolution kernel is separated into multiple convolution kernels. The network is trained stage by stage, so network training converges faster. It effectively solved the problem that the number of parameters increase sharply with the added network training difficulty due to the widened dimension of standard 3D convolution. The experimental results show that the medical images reconstructed by the P3DSRNet model have clearer texture details and better visual effects, compared with those by the traditional interpolation and LRTV super-resolution algorithms. The P3DSRNet model has fewer network parameters, compared with the SRCNN3D and ReCNN super-resolution algorithms. In addition, the SR image PSNRs increase by 1.88 dB, 0.30 dB, and the SSIMs increase by 0.009 6, 0.001 1, respectively, compared with SRCNN3D and ReCNN. The P3DSRNet not only reduces the number of parameters and computational complexity, but also improves the super-resolution performances of medical images.

Research on Computer Multimedia Software Graphics and Image Processing and Analysis System in Colleges

With the development of computer software and hardware technology in colleges and universities, the application of multimedia computers has become increasingly widespread. Graphics and image processing occupy a major position in the application of multimedia computers in colleges and universities. Image display is the basis of image processing. The key of special effect display is to divide the image into different image area blocks. The usual image special effect display method is to divide the image into different image blocks according to certain rules. Based on the analysis of the principle of image special effects display, this paper proposes a template-based image special effects display method. Experimental results show that compared with traditional interpolation algorithms, such as bilinear interpolation and bicubic polynomial interpolation, this algorithm has better interpolation effects and overcomes the speckle interference in single wavelet interpolation.

A Bio-inspired trajectory planning method for robotic manipulators based on improved bacteria foraging optimization algorithm and tau theory.

In this paper, a novel bio-inspired trajectory planning method is proposed for robotic systems based on an improved bacteria foraging optimization algorithm (IBFOA) and an improved intrinsic Tau jerk (named Tau-J*) guidance strategy. Besides, the adaptive factor and elite-preservation strategy are employed to facilitate the IBFOA, and an improved Tau-J* with higher-order of intrinsic guidance movement is used to avoid the nonzero initial and final jerk, so as to overcome the computational burden and unsmooth trajectory problems existing in the optimization algorithm and traditional interpolation algorithm. The IBFOA is utilized to determine a small set of optimal control points, and Tau-J* is then invoked to generate smooth trajectories between the control points. Finally, the results of simulation tests demonstrate the eminent stability, optimality, and rapidity capability of the proposed bio-inspired trajectory planning method.

Hyperspectral Imagery Spatial Super-Resolution Using Generative Adversarial Network

Hyperspectral imagery contains both spatial structure information and abundant spectral features of imaged objects. However, due to sensor limitations, abundant spectral information always comes at the sacrifice of low spatial resolution, which brings about difficulties with object analysis and identification. The super-resolution (SR) of HSIs, restored by the traditional interpolation algorithms or the network models trained with the mean-square-error-based loss function, tends to produce over-smoothed images. In this paper, we propose a novel Hyperspectral imagery Spatial Super-Resolution algorithm based on a Generative Adversarial Network (HSSRGAN). The generator network in HSSRGAN consists of two interacting part, i.e., a spatial feature enhanced network (SFEN) and a spectral refined network (SRN), while the discriminator network is employed to predict the probability that the authentic HR image is comparatively more similar than the forged generated image. Concretely, SFEN with the special dense residual blocks is designed to fully extract and enhance more deep hierarchical spatial features of hyperspectral imagery, while SRN is constructed to capture spectral interrelationships and refine the spatial context information so as to increase spatial resolution and alleviate spectral distortion. Moreover, SFEN and SRN are trained by the least-absolute-deviation based loss function to investigate spatial context and the spectral-angle-mapper based loss function to refine spectral information. We validate two versions of our proposed algorithm, 3D-HSSRGAN and 2D-HSSRGAN, on Pavia Centre dataset and Cuprite dataset. Experimental results show that the presented approach is superior to several existing state-of-the-art works.

A Process-Oriented Method for Rapid Acquisition of Canopy Height Model From RGB Point Cloud in Semiarid Region

This study addressed the problems of difficult parameter selection of ground point filtering algorithm, slow speed, and susceptibility to wrong classification points of traditional interpolation algorithm in extracting canopy height model (CHM) from dense point cloud produced by structure-from-motion (SfM). Finding the true ground is vital for canopy height estimation using UAV RGB point cloud, especially for semi-arid area where cloud points of short vegetation like shrubs and dwarf trees generally mixed up with the ground points. This paper proposes a ground point extraction strategy that combines the advantages of structural and spectral filtering. Specifically, spectral filtering simplifies the threshold selection for structural filtering, while structural filtering removes the outliers from spectral filtering. Considering the misclassified points in the ground filtering algorithm, a fast, nonparametric ground point interpolation algorithm was used to suppress the wrong classification points. This novel algorithm is based on the basic idea of least square quadratic fitting and prediction of terrain profile (PF). The quantitative results show that compared to Inverse Distance Weighting (IDW), Radial Basis Function (RBF), and Ordinary Kriging (OK), it takes less time (PF: 122s, IDW: 518s, RBF: 1374s, OK: 1129s), and has lower RMSE (PF: 0.301 m, IDW: 0.549 m, RBF: 0.903m, OK: 0.427m).

AN ADAPTIVE IMAGE SCALING ALGORITHM BASED ON CONTINUOUS FRACTION INTERPOLATION AND MULTI-RESOLUTION HIERARCHY PROCESSING

Traditional interpolation algorithms often blur the edges of the target image due to low-pass filtering effects, making it difficult to obtain satisfactory visual effects. Especially when the reduction ratio becomes small, the phenomenon of jagged edges and partial information loss will occur. In order to obtain better image scaling quality, an adaptive image scaling algorithm based on continuous fraction interpolation and multi-resolution hierarchical processing is proposed. In order to overcome the noise problem of the original image, this paper first performs wavelet decomposition on the original image to obtain multiple images with different resolutions. Secondly, in order to eliminate the influence of local area variance on the overall image, weighted average is performed on images of different resolutions. Then, in order to overcome the blurring effect of the weighted average image, by calculating the variance of the three groups of pixels around the target pixel, selecting a group of pixels with the smallest variance and using the Salzer continuous fraction interpolation equation to obtain the gray value of the target pixel. Finally, the multiple corrected images are stitched together into a scaled image. The algorithm in this paper achieves a high-order smooth transition between pixels in the same feature area, and can also adaptively modify the pixels of the image. The experimental results show that the edge of the target image obtained by the algorithm in this paper is clear, and the algorithm complexity is low, which is convenient for hardware implementation and can realize real-time image scaling.

Multiframe interpolation for video using phase features

Traditional frame interpolation algorithms typically find dense correspondences to synthesize an in-between frame. Finding correspondences is often sensitive to occlusion, disocclusion, and changes in color or luminance. We present a phase-feature-aided multiframe interpolation network that aims to estimate multiple in-between frames in one pass and handle challenging scenarios such as extreme light changes and occlusion. We first model the relation between multiple in-between frames together to enhance the temporal consistency. Two candidate optical flow fields are produced for a given in-between frame, one predicted from our network and the other estimated from those of neighboring frames using a flow fusion map. We also employ an image fusion map to combat occlusion problems in the warping processes, producing two candidate interpolated images that are fed to a shallow network with a residual structure to obtain the final interpolated image. To handle challenging scenarios, we apply a set of Gabor filters to extract phase variations in the feature domain with a multiscale phase subnetwork. Our entire neural network is end-to-end trainable. Our experiments show that this method outperforms the state-of-the-art approaches and achieves marked visual improvement in challenging scenarios.

Super Resolution Reconstruction of Images Based on Interpolation and Full Convolutional Neural Network and Application in Medical Fields

The traditional image to enlarge algorithms include nearest neighbor interpolation, bilinear interpolation and high-order interpolation. In order to achieve super-resolution reconstruction of images, a new algorithm combining traditional algorithms and deep learning is proposed. The framework is divided into two parts. Firstly, the deep reconstruction of the low-resolution data is performed by the ability of deep learning to extract features automatically. Then, combining with the traditional interpolation reconstruction results, the deep learning algorithm is used again for training and learning, and finally the high-resolution reconstructed data is obtained. The algorithm is validated using an online public test data set. The results show that the algorithm has a significant effect on the MSE (mean squared error) and PSNR (Peak Signal to Noise Ratio). Compared with the traditional interpolation algorithm and the single deep learning algorithm, the proposed algorithm has higher performance. Moreover, the proposed algorithm is perfect for the reconstruction of the details, the outline is clear, and the high-quality image is obtained.

Interpolation algorithm considering simultaneous solution and instantaneous solution for power electronics electromagnetic transient simulation

In order to correctly simulate the simultaneous switching of power electronics circuits and to solve the problems of virtual power loss existing in the traditional interpolation algorithm, this article proposes interpolation algorithm considering simultaneous solution and instantaneous solution. After each integration, it searches for the switching events and determines the simultaneous switching. The simultaneous switching events are solved simultaneously. Instantaneous solution is carried out at forced commutation switching instant. The historical terms calculation method in instantaneous solution under different conditions is given, and the exact power loss of forced commutation switch is obtained. After processing all switching events during one time-step, the half-step interpolation is performed to eliminate numerical oscillations. The proposed algorithm is applied to the electromagnetic transient programme of advanced digital power system simulator (ADPSS), and the correctness and effectiveness of the algorithm are verified by simulation tests for typical power electronics circuits. The simulation results show that the proposed algorithm has high simulation accuracy and can satisfy requirements of power electronics simulation.

An improved ionosphere interpolation algorithm for network RTK in low-latitude regions

A traditional interpolation algorithm with the linear interpolation method (LIM) using a fixed number of reference stations is widely used in network RTK to obtain the ionospheric delays for the users. In low-latitude regions, where the ionosphere is relatively active, however, large interpolation errors exist, especially for satellites at low elevation angles. Considering the characteristics of “coinciding ionospheric pierce points (CIPPs)” with a similar nature of ionospheric delays, an improved interpolation algorithm is proposed. In this algorithm, all stations with CIPPs are used to establish the interpolation model; thus, more precise interpolation model is achieved. To validate the performance of the proposed algorithm, data from some reference stations in Guangdong Province of China were used, and the results are compared with those with the traditional interpolation algorithm. Numerical analysis shows that the interpolation accuracy of the proposed algorithm increases by 10–30% compared with the traditional one. Since the number of reference stations is flexible, the proposed algorithm can also balance the model accuracy with the computation burdens. In addition, the proposed algorithm is less affected by the selection of master reference station. In terms of network RTK on-the-fly positioning, the time-to-first-fix is reduced when replacing the traditional interpolation algorithm with the proposed one.

The Application of a Complex Composite Fractal Interpolation Algorithm in the Seabed Terrain Simulation

Seabed terrain modelling is one of the key technologies in the Subsea Environmental Information System, and this system is critical for underwater vehicle path planning. A composite fractal interpolation algorithm based on improved fractional Brownian motion (FBM) and an improved iterative function system (IFS) is proposed in this paper to increase the precision of the seabed terrain model for submarine topography and to account for the complexity and irregularity of fractal properties in each region. The MATLAB simulation experiment showed that fractal properties of the model built by the complex composite fractal interpolation algorithm were closer to real surface features. After calculation analysis, the model built by the complex composite fractal interpolation algorithm, when compared with the model built by the traditional interpolation algorithm or by the single fractal interpolation algorithm, had higher precision and was more suitable for path planning for underwater vehicles.

Mapping of bioavailable strontium isotope ratios in France for archaeological provenance studies

Strontium isotope ratios (87Sr/86Sr) of archaeological samples (teeth and bones) can be used to track mobility and migration across geologically distinct landscapes. However, traditional interpolation algorithms and classification approaches used to generate Sr isoscapes are often limited in predicting multiscale 87Sr/86Sr patterning. Here we investigate the suitability of plant samples and soil leachates from the IRHUM database (www.irhumdatabase.com) to create a bioavailable 87Sr/86Sr map using a novel geostatistical framework. First, we generated an 87Sr/86Sr map by classifying 87Sr/86Sr values into five geologically-representative isotope groups using cluster analysis. The isotope groups were then used as a covariate in kriging to integrate prior geological knowledge of Sr cycling with the information contained in the bioavailable dataset and enhance 87Sr/86Sr predictions. Our approach couples the strengths of classification and geostatistical methods to generate more accurate 87Sr/86Sr predictions (Root Mean Squared Error = 0.0029) with an estimate of spatial uncertainty based on lithology and sample density. This bioavailable Sr isoscape is applicable for provenance studies in France, and the method is transferable to other areas with high sampling density. While our method is a step forward in generating accurate 87Sr/86Sr isoscapes, the remaining uncertainty also demonstrates that fine-modelling of 87Sr/86Sr variability is challenging and requires more than geological maps for accurately predicting 87Sr/86Sr variations across the landscape. Future efforts should focus on increasing sampling density and developing predictive models to further quantify and predict the processes that lead to 87Sr/86Sr variability.

Estimation of Ground PM2.5 Concentrations using a DEM-assisted Information Diffusion Algorithm: A Case Study in China

When estimating national PM2.5 concentrations, the results of traditional interpolation algorithms are unreliable due to a lack of monitoring sites and heterogeneous spatial distributions. PM2.5 spatial distribution is strongly correlated to elevation, and the information diffusion algorithm has been shown to be highly reliable when dealing with sparse data interpolation issues. Therefore, to overcome the disadvantages of traditional algorithms, we proposed a method combining elevation data with the information diffusion algorithm. Firstly, a digital elevation model (DEM) was used to segment the study area into multiple scales. Then, the information diffusion algorithm was applied in each region to estimate the ground PM2.5 concentration, which was compared with estimation results using the Ordinary Kriging and Inverse Distance Weighted algorithms. The results showed that: (1) reliable estimate at local area was obtained using the DEM-assisted information diffusion algorithm; (2) the information diffusion algorithm was more applicable for estimating daily average PM2.5 concentrations due to the advantage in noise data; (3) the information diffusion algorithm required less supplementary data and was suitable for simulating the diffusion of air pollutants. We still expect a new comprehensive model integrating more factors would be developed in the future to optimize the interpretation accuracy of short time observation data.

Geostatistical Evaluation of Spatial Variation Related to Groundwater Quality Database: Case Study for Arak Plain Aquifer, Iran

Geostatistical methods are one of the advanced techniques to interpolate groundwater quality data. Geostatistical interpolation techniques employ both the mathematical and the statistical properties of the measured points. Compiling the data distribution on spatial and temporal domain is of crucial importance in order to evaluate its quality and safety. The main purpose of this paper is to assess groundwater quality of Arak plain, Iran, by an unbiased interpolated method so called Kriging. Therefore, seven quality variables of Arak plain aquifer including TDS, SAR, EC, Na+, TH, Cl−, and SO42− have been analyzed, studied, and interpreted statistically and geostatistically. Utilized data in this study were collected from 97 water well samples in Arak plain, in 2012. After normalizing data, variogram as a geostatistical tool for defining spatial regression was calculated and experimental variograms have been plotted by GS+ software, then the best theoretical model was fitted to each variogram based on minimum RSS error. Cross validation was used to determine the accuracy of the estimated data. The uncertainty of the method could be well assessed via this method since the method not only gave the average error (around 0 in this study) but also gave the standard deviation of the estimations. Therefore, more than 3800 points were estimated by ordinary Kriging algorithm in places which have not been sampled. Finally, estimation maps of groundwater quality were prepared and map of estimation variance, EV, has been presented to assess the quality of estimation in each estimated point. Results showed that the Kriging method is more accurate than the traditional interpolation algorithms not honoring the spatial properties of the database.

A Fast Interpolation Algorithm Based on the Fractal in the Digital Speckle Sub-Pixel Displacement Measurement

The Digital Speckle Correlation Method is a non-contact in-plane displacement measurement method based on machine vision. The spline, the linear or the bi-cubic interpolation algorithm is often used in the sub-pixel algorithm. The block effect or the edges blurring, however, could occur in these algorithms. Therefore, a fast interpolation algorithm based on fractal theory is proposed in this paper. The results indicate that this algorithm can overcome either the blocking effect or the edge blurring caused by the traditional interpolation algorithm. In addition, with the same sub-pixel accuracy, the shortest time is required.