Gaussian Smoothing Filter Research Articles

Morphological analysis of 3d atom probe data using Minkowski functionals

We present a morphological analysis of atom probe data of nanoscale microstructural features, using methods developed by the astrophysics community to describe the shape of superclusters of galaxies. We describe second-phase regions using Minkowski functionals, representing the regions’ volume, surface area, mean curvature and Euler characteristic. The alloy data in this work show microstructures that can be described as sponge-like, filament-like, plate-like, and sphere-like at different concentration levels, and we find quantitative measurements of these features. To reduce user decision-making in constructing isosurfaces and to enhance the accuracy of the analysis a maximum likelihood based denoising filter was developed. We show that this filter performs significantly better than a simple Gaussian smoothing filter. We also interpolate the data using natural cubic splines, to refine voxel sizes and to refine the surface. We demonstrate that it is possible to find a mathematically well-defined, quantitative description of microstructure from atomistic datasets, to sub-voxel resolution, without user-tuneable parameters.

Research on Improved Gaussian Smoothing Filters for SLAM Application

To address the navigation issues of the planetary rover and construct a map for the unknown environment as well as the surface of the planets in our solar system, the simultaneous localization and mapping can be seen as an alternative method. In terms of the navigation with the laser sensor, the Kalman filter and its improving algorithms, such as EKF and UKF are widely used in the the process of processing information. Nevertheless, these filter algorithms suffer from low accuracy and significant computation expensive. The EKF algorithm has a linearization process, the UKF algorithm is better matched in a nonlinear system than the EKF algorithm, but it has more computational complexity. The GP-RTSS filtering algorithm, based on a Gaussian filter, is significantly superior to the EKF and UKF algorithms regarding the sensor fusion accuracy. The Gaussian Process can be used in different non-linear system, does not need prediction model and linearization. However, the main barrier in the process of implementing the GP-RTSS algorithm is that the Gaussian core function requires a lot of computation. In this paper, an algorithm, so-called DIS RTSS filter under a distributed computation scheme, derived from the GP-RTSS Gaussia n smoothing and filter, is proposed. The distributed system can effectively reduce the cost of computation (computation expense and memory). Moreover, four fusion methods for the DIS RTSS filter, i.e., DIS RTP, DIS RTGP, DIS RTB, DIS RTrB are discussed in this paper. The experiments show that among the four algorithms described above, the DIS RTGP algorithm is the most effective solution for practical implementation. The DIS RTSS filtering algorithm can realize a high processing rate and can theoretically process an infinite number of data samples.

Investigating the Impact of Image Content on the Energy Efficiency of Hardware-accelerated Digital Spatial Filters

Battery-operated low-power portable computing devices are becoming an inseparable part of human daily life. One of the major goals is to achieve the longest battery life in such a device. Additionally, the need for performance in processing multimedia content is ever increasing. Processing image and video content consume more power than other applications. A widely used approach to improving energy efficiency is to implement the computationally intensive functions as digital hardware accelerators. Spatial filtering is one of the most commonly used methods of digital image processing. As per the Fourier theory, an image can be considered as a two-dimensional signal that is composed of spatially extended two-dimensional sinusoidal patterns called gratings. Spatial frequency theory states that sinusoidal gratings can be characterised by its spatial frequency, phase, amplitude, and orientation. This article presents results from our investigation into assessing the impact of these characteristics of a digital image on the energy efficiency of hardware-accelerated spatial filters employed to process the same image. Two greyscale images each of size 128 × 128 pixels comprising two-dimensional sinusoidal gratings at maximum spatial frequency of 64 cycles per image orientated at 0° and 90°, respectively, were processed in a hardware implemented Gaussian smoothing filter. The energy efficiency of the filter was compared with the baseline energy efficiency of processing a featureless plain black image. The results show that energy efficiency of the filter drops to 12.5% when the gratings are orientated at 0° whilst rises to 72.38% at 90°.

Improving In-Situ Estimation of Soil Profile Properties Using a Multi-Sensor Probe.

Optical diffuse reflectance spectroscopy (DRS) has been used for estimating soil physical and chemical properties in the laboratory. In-situ DRS measurements offer the potential for rapid, reliable, non-destructive, and low cost measurement of soil properties in the field. In this study, conducted on two central Missouri fields in 2016, a commercial soil profile instrument, the Veris P4000, acquired visible and near-infrared (VNIR) spectra (343–2222 nm), apparent electrical conductivity (ECa), cone index (CI) penetrometer readings, and depth data, simultaneously to a 1 m depth using a vertical probe. Simultaneously, soil core samples were obtained and soil properties were measured in the laboratory. Soil properties were estimated using VNIR spectra alone and in combination with depth, ECa, and CI (DECS). Estimated soil properties included soil organic carbon (SOC), total nitrogen (TN), moisture, soil texture (clay, silt, and sand), cation exchange capacity (CEC), calcium (Ca), magnesium (Mg), potassium (K), and pH. Multiple preprocessing techniques and calibration methods were applied to the spectral data and evaluated. Calibration methods included partial least squares regression (PLSR), neural networks, regression trees, and random forests. For most soil properties, the best model performance was obtained with the combination of preprocessing with a Gaussian smoothing filter and analysis by PLSR. In addition, DECS improved estimation of silt, sand, CEC, Ca, and Mg over VNIR spectra alone; however, the improvement was more than 5% only for Ca. Finally, differences in estimation accuracy were observed between the two fields despite them having similar soils, with one field demonstrating better results for all soil properties except silt. Overall, this study demonstrates the potential for in-situ estimation of profile soil properties using a multi-sensor approach, and provides suggestions regarding the best combination of sensors, preprocessing, and modeling techniques for in-situ estimation of profile soil properties.

Enhanced see-through near-eye display using time-division multiplexing of a Maxwellian-view and holographic display.

In this paper, we suggest the improved integration of a holographic display and a Maxwellian-view display using time-division multiplexing and describe an image rendering process for the proposed system. In general, the holographic displays have a resolution limit when used to represent a virtual 3D scene. In the proposed system, the holographic display processed relatively few layers of the virtual 3D scene, while the remaining objects were processed with a Maxwellian-view display to which was applied a Gaussian smoothing filter. Hence, we obtained the retaining holographic image quality, expanding the field of view, and reducing the computation time of the proposed system. The holographic display of the proposed system had an image size of 28 mm × 28 mm with a field of view of 1.02° and a 10.8 mm eye box. The Maxwellian-view display had an image size of 230 mm × 230 mm with a field of view of 22.6 ° and a 0.9 mm eye box diameter. Each display was integrated in time-division multiplexing of 40 Hz, and the proposed system was successfully verified.

Damage detection in carbon fiber–reinforced polymer composite via electrical resistance tomography with Gaussian anisotropic regularization

Electrical resistance tomography is a method for sensing the spatial distribution of electrical conductivity. Therefore, this type of tomography is suitable for sensing damages, which affect electrical conductivity. The utilization of resistance tomography for the structural health monitoring of carbon fiber–reinforced polymer composites is questionable owing to its low spatial resolution and the strong anisotropy of carbon fiber–reinforced polymer composites. This article deals with the employment of resistance tomography with regularization based on a Gaussian anisotropic smoothing filter for the detection of cuts. The advantages of the filter are shown through the image reconstruction of rectangular composite specimens with three different laminate stacking sequences. The cuts are implemented by a milled groove. Visual comparison of the images shows a substantial improvement in the shape reconstruction ability. In addition to visual comparison, the image reconstructions are assessed in terms of the reconstruction error and cross-correlation.

Adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain for scanning electron microscope images.

Image processing is introduced to remove or reduce the noise and unwanted signal that deteriorate the quality of an image. Here, a single level two-dimensional wavelet transform is applied to the image in order to obtain the wavelet transform sub-band signal of an image. An estimation technique to predict the noise variance in an image is proposed, which is then fed into a Wiener filter to filter away the noise from the sub-band of the image. The proposed filter is called adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in the wavelet domain. The performance of this filter is compared with four existing filters: median filter, Gaussian smoothing filter, two level wavelet transform with Wiener filter and adaptive noise Wiener filter. Based on the results, the adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain has better performance than the other four methods.

FPGA Implementation of a Novel Gaussian Filter Using Power Optimized Approximate Adders

Smoothing filters are essential for noise removal and image restoration. Gaussian filters are used in many digital image and video processing systems. Hence the hardware implementation of the Gaussian filter becomes a reliable solution for real time image processing applications. This paper discusses the implementation of a novel Gaussian smoothing filter with low power approximate adders in Field Programmable Gate Array (FPGA). The proposed Gaussian filter is applied to restore the noisy images in the proposed system. Original test images with 512x512 pixels were taken and divided in to 4x4 blocks with 256x256 pixels. The proposed technique has been applied and the performance metrics were measured for various simulation criteria. The proposed algorithm is also implemented using approximate adders, since approximate adders had been recognized as a reliable alternate for error tolerant applications in circuit based metrics such as power, area and delay where the accuracy may be considered for trade off.

An error-efficient Gaussian filter for image processing by using the expanded operand decomposition logarithm multiplication

Digital signal processing and image processing applications require the noise free and efficient arithmetic operations. To overcome the noise problem, an efficient expanded operand decomposition (ExOD) based logarithm multiplication is proposed and applied for convolution of Gaussian smoothing filter to minimize the noise. The ExOD approach improves the average error percentage (AEP), mean square error (MSE) and the error rate in comparison of reported operand decomposition (OD) and improved operand decomposition (IOD). The peak signal-to-noise ratio (PSNR), MSE and the AEP of the ExOD logarithm multiplication method are improved as compared to the reported logarithm multiplication to the convolution operation. The ExOD method for logarithmic multiplication gives AEP of 0.276, 0.294 and 0.309%, whereas Mitchell’s algorithm gives 2.702, 2.661 and 2.830%, OD-Mitchell’s multiplication gives 1.441, 1.449 and 2.170% and IOD-Mitchell’s multiplication gives 1.627, 1.678 and 2.064% for 4-, 8-, and 16-bit operations respectively. ExOD-Mitchell’s multiplication gives 1.867% MSE, 45.453 db PSNR and 0.293% AEP, whereas Mitchell’s algorithm gives 18.688% MSE, 35.449 db PSNR and 2.731% AEP, OD-Mitchell’s multiplication gives 9.877% MSE, 38.218 db PSNR and 1.686% AEP and IOD-Mitchell’s multiplication gives 10.866% MSE, 37.804 db PSNR and 1.789% AEP. The proposed ExOD based Mitchell algorithm multiplication has useful applications in image processing with efficient accuracy.

Energy Efficient Canny Edge Detector for Advanced Mobile Vision Applications

In this paper, we present an energy-efficient architecture of the Canny edge detector for advanced mobile vision applications. Three key techniques for reducing computational complexity of the Canny edge detector are presented. First, by exploiting the rank characteristic of the convolution kernel of Gaussian smoothing and Sobel gradient filters, common computations are identified and shared in the image filter design to reduce the number of additions and multiplications. For the gradient magnitude/direction computation, only three directions of neighboring pixels are considered to reduce computation energy with minor degradation on conformance performance (CP). For the adaptive threshold selections, an interesting observation is that the mean values of gradient magnitudes show small variations depending on the classified block types. Thus, the threshold selection process can be simplified as multiplying the mean value of the local block with predecided constants. The proposed low complexity Canny edge detector has been implemented using both field-programmable gate arrays (FPGAs) and a 65-nm standard-cell library. The FPGA implementation with Xilinx Virtex-V (XC5VSX240T) shows that our edge detector achieves 48% of area and 73% of execution time savings over the conventional architecture without seriously sacrificing the detection performance. The proposed edge detector implemented with 65-nm standard-cell library can easily support real-time ultrahigh definition video data processing (50 frames/s) with the power consumption of 5.48 mW (108.84 $\mu \text{J}$ /frame).

Denoising of X-ray Images Using the Adaptive Algorithm Based on the LPA-RICI Algorithm

Diagnostics and treatments of numerous diseases are highly dependent on the quality of captured medical images. However, noise (during both acquisition and transmission) is one of the main factors that reduce their quality. This paper proposes an adaptive image denoising algorithm applied to enhance X-ray images. The algorithm is based on the modification of the intersection of confidence intervals (ICI) rule, called relative intersection of confidence intervals (RICI) rule. For each image pixel apart, a 2D mask of adaptive size and shape is calculated and used in designing the 2D local polynomial approximation (LPA) filters for noise removal. One of the advantages of the proposed method is the fact that the estimation of the noise free pixel is performed independently for each image pixel and thus, the method is applicable for easy parallelization in order to improve its computational efficiency. The proposed method was compared to the Gaussian smoothing filters, total variation denoising and fixed size median filtering and was shown to outperform them both visually and in terms of the peak signal-to-noise ratio (PSNR) by up to 7.99 dB.

Design of intelligent retrieval algorithm for literature information resources in digital library

The intelligent retrieval method of information resources is the key technology of literature information search in digital library. The detection results directly determine the performance of subsequent literature information search. Therefore, an intelligent retrieval algorithm for literature information resources in digital library is designed. Firstly, the Gaussian smoothing filter and the wavelet are used to process the literature information resources in digital library to hide the useless information, and construct a new literature information resource signal in digital library. Secondly, the information is divided into the target information and the database information through the threshold. The iterative method is used to retrieve the optimal threshold of digital literature information, and the structural elements of literature information resources in digital library under two different scales are weighted and combined to obtain the final literature information resources and intelligently detected. Finally, the simulation results show that the designed algorithm can accurately extract the literature information resources in digital library, and has a wide range of practicability and application value.

Image processing for synthesis imaging of mingantu spectral radioheliograph (MUSER)

MingantU SpEctral Radioheliograph (MUSER) can generate the images of the Sun with high time, frequency and spatial resolutions. It employs the aperture synthesis (AS) principle to image the Sun with plentiful solar radio activities. Different from general imaging system, AS records sparse Fourier components of the spatial image of the Sun by recurring to electromagnetic interference imaging principle. However, due to the limited number of antennas, the recorded Fourier components is extremely sparse, which results in very blurring images. This problem is equivalent to convoluting an image with a Gaussian smoothing filter in spatial domain. Accordingly, one can recover an image from its burred version by inverse operation of convolution, namely deconvolution, which was widely known as CLEAN algorithm. This algorithm however does not perform well on solar images characterized by extended source instead point source. In this paper, a new method based on compressed sensing (CS) is proposed to replace CLEAN for imaging or preceded by CLEAN. It describes itself a standard optimization function constrained by sparseness. Specifically, it adopts structural group dictionary to represent solar images for exploring both local sparsity and nonlocal self-similarity. We also investigate image reconstruction of MUSER in a wider range by employing several state-of-the-art image deburring methods. The performance analysis reveals that the proposed method contributes image quality improvement of MUSER markedly beyond the other methods.

ACM: An Energy-Efficient Accuracy Configurable Multiplier for Error-Resilient Applications

The multimedia applications such as image, audio and video processing allow approximation in computations, provided that, errors are of definite types and have austerities within confined limits, thus exhibiting error-resiliency. An approximate arithmetic circuit can be exploited to avail this error-resiliency for improving energy-efficiency. This paper presents an approximate multiplier that provides higher energy-efficiency at the cost of minor loss of accuracy. The proposed multiplier offers twofold improved performance because of reduced level of gates and curtailed inherent switched capacitances. Further, to achieve variable accuracy, an Accuracy Configurable Multiplier (ACM) algorithm is proposed that provides improved Speed-Power-Area-Accuracy (SPAA) metrics. The proposed ACM enables dynamic accuracy configurability via small error correction logic. Simulation results over accurate 8-bit multiplier indicate 57.37% and 25.17% reduced power and area, respectively. Moreover, accuracy configurability is achieved with only 10.5% and 12.32%, area and power overhead, respectively. Moreover, the proposed multiplier in real applications such as Gaussian smoothing filter attains better SPAA tradeoff over the existing approximate multipliers.

Multiple Visual Features Measurement With Gradient Domain Guided Filtering for Multisensor Image Fusion

Multisensor image fusion technologies, which convey image information from different sensor modalities to a single image, have been a growing interest in recent research. In this paper, we propose a novel multisensor image fusion method based on multiple visual features measurement with gradient domain guided filtering. First, a Gaussian smoothing filter is employed to decompose each source image into two components: approximate component formed by homogeneous regions and detail component with sharp edges. Second, an effective decision map construction model is presented by measuring three key visual features of the input sensor image: contrast saliency, sharpness, and structure saliency. Third, a gradient domain guided filtering-based decision map optimization technique is proposed to make full use of spatial consistency and generate weight maps. Finally, the resultant image is fused with the weight maps and then is experimentally verified through multifocus image, multimodal medical image, and infrared-visible image fusion. The experimental results demonstrate that the proposed method can achieve better performance than state-of-the-art methods in terms of subjective visual effect and objective evaluation.

Full-waveform LiDAR echo decomposition based on wavelet decomposition and particle swarm optimization

To measure the distances and properties of the objects within a laser footprint, a decomposition method for full-waveform light detection and ranging (LiDAR) echoes is proposed. In this method, firstly, wavelet decomposition is used to filter the noise and estimate the noise level in a full-waveform echo. Secondly, peak and inflection points of the filtered full-waveform echo are used to detect the echo components in the filtered full-waveform echo. Lastly, particle swarm optimization (PSO) is used to remove the noise-caused echo components and optimize the parameters of the most probable echo components. Simulation results show that the wavelet-decomposition-based filter is of the best improvement of SNR and decomposition success rates than Wiener and Gaussian smoothing filters. In addition, the noise level estimated using wavelet-decomposition-based filter is more accurate than those estimated using other two commonly used methods. Experiments were carried out to evaluate the proposed method that was compared with our previous method (called GS-LM for short). In experiments, a lab-build full-waveform LiDAR system was utilized to provide eight types of full-waveform echoes scattered from three objects at different distances. Experimental results show that the proposed method has higher success rates for decomposition of full-waveform echoes and more accurate parameters estimation for echo components than those of GS-LM. The proposed method based on wavelet decomposition and PSO is valid to decompose the more complicated full-waveform echoes for estimating the multi-level distances of the objects and measuring the properties of the objects in a laser footprint.

Modeling the reconstructed BAO in Fourier space

The density field reconstruction technique, which was developed to partially reverse the nonlinear degradation of the Baryon Acoustic Oscillation (BAO) feature in the galaxy redshift surveys, has been successful in substantially improving the cosmology constraints from recent galaxy surveys such as Baryon Oscillation Spectroscopic Survey (BOSS). We estimate the efficiency of the reconstruction method as a function of various reconstruction details. To directly quantify the BAO information in nonlinear density fields before and after reconstruction, we calculate the cross-correlations (i.e., propagators) of the pre(post)-reconstructed density field with the initial linear field using a mock galaxy sample that is designed to mimic the clustering of the BOSS CMASS galaxies. The results directly provide the BAO damping as a function of wavenumber that can be implemented into the Fisher matrix analysis. We focus on investigating the dependence of the propagator on a choice of smoothing filters and on two major different conventions of the redshift-space density field reconstruction that have been used in literature. By estimating the BAO signal-to-noise for each case, we predict constraints on the angular diameter distance and Hubble parameter using the Fisher matrix analysis. We thus determine an optimal Gaussian smoothing filter scale for the signal-to-noise level of the BOSS CMASS. We also present appropriate BAO fitting models for different reconstruction methods based on the first and second order Lagrangian perturbation theory in Fourier space. Using the mock data, we show that the modified BAO fitting model can substantially improve the accuracy of the BAO position in the best fits as well as the goodness of the fits.

Adaptive noise Wiener filter for scanning electron microscope imaging system.

Noise on scanning electron microscope (SEM) images is studied. Gaussian noise is the most common type of noise in SEM image. We developed a new noise reduction filter based on the Wiener filter. We compared the performance of this new filter namely adaptive noise Wiener (ANW) filter, with four common existing filters as well as average filter, median filter, Gaussian smoothing filter and the Wiener filter. Based on the experiments results the proposed new filter has better performance on different noise variance comparing to the other existing noise removal filters in the experiments.

SU‐E‐J‐261: The Importance of Appropriate Image Preprocessing to Augment the Information of Radiomics Image Features

Purpose:To investigate how different image preprocessing techniques, their parameters, and the different boundary handling techniques can augment the information of features and improve feature's differentiating capability.Methods:Twenty‐seven NSCLC patients with a solid tumor volume and no visually obvious necrotic regions in the simulation CT images were identified. Fourteen of these patients had a necrotic region visible in their pre‐treatment PET images (necrosis group), and thirteen had no visible necrotic region in the pre‐treatment PET images (non‐necrosis group). We investigated how image preprocessing can impact the ability of radiomics image features extracted from the CT to differentiate between two groups. It is expected the histogram in the necrosis group is more negatively skewed, and the uniformity from the necrosis group is less. Therefore, we analyzed two first order features, skewness and uniformity, on the image inside the GTV in the intensity range [−20HU, 180HU] under the combination of several image preprocessing techniques: (1) applying the isotropic Gaussian or anisotropic diffusion smoothing filter with a range of parameter(Gaussian smoothing: size=11, sigma=0:0.1:2.3; anisotropic smoothing: iteration=4, kappa=0:10:110); (2) applying the boundaryadapted Laplacian filter; and (3) applying the adaptive upper threshold for the intensity range. A 2‐tailed T‐test was used to evaluate the differentiating capability of CT features on pre‐treatment PT necrosis.Result:Without any preprocessing, no differences in either skewness or uniformity were observed between two groups. After applying appropriate Gaussian filters (sigma>=1.3) or anisotropic filters(kappa >=60) with the adaptive upper threshold, skewness was significantly more negative in the necrosis group(p<0.05). By applying the boundary‐adapted Laplacian filtering after the appropriate Gaussian filters (0.5 <=sigma<=1.1) or anisotropic filters(20<=kappa <=50), the uniformity was significantly lower in the necrosis group (p<0.05).Conclusion:Appropriate selection of image preprocessing techniques allows radiomics features to extract more useful information and thereby improve prediction models based on these features.

A 4-D Subgrid Scheme for the NS-FDTD Technique Using the CNS-FDTD Algorithm With the Shepard Method and a Gaussian Smoothing Filter

A precise subgrid technique for the nonstandard finite-difference time-domain (NS-FDTD) method in 4-D (i.e., 3-D space and 1-D time) is proposed in this paper. The novel algorithm is efficiently blended with the Shepard scheme and a Gaussian smoothing filter to minimize the error in the interpolated values used for the spatial connection process. Moreover, the required time interpolation is performed via the complex (C)NS-FDTD approach. A key advantage of the proposed formulation is its structural simplicity, due to the prior interpolation concepts and the absence of any nonphysical convention, which enables its straightforward application to a variety of realistic problems. The numerical results validate the benefits of the method by means of different subgrid simulation scenarios.