Gaussian Low-pass Filter Research Articles

Hyperspectral Anomaly Detection Using Reconstruction Fusion of Quaternion Frequency Domain Analysis.

Most existing techniques consider hyperspectral anomaly detection (HAD) as background modeling and anomaly search problems in the spatial domain. In this article, we model the background in the frequency domain and treat anomaly detection as a frequency-domain analysis problem. We illustrate that spikes in the amplitude spectrum correspond to the background, and a Gaussian low-pass filter performing on the amplitude spectrum is equivalent to an anomaly detector. The initial anomaly detection map is obtained by the reconstruction with the filtered amplitude and the raw phase spectrum. To further suppress the nonanomaly high-frequency detailed information, we illustrate that the phase spectrum is critical information to perceive the spatial saliency of anomalies. The saliency-aware map obtained by phase-only reconstruction (POR) is used to enhance the initial anomaly map, which realizes a significant improvement in background suppression. In addition to the standard Fourier transform (FT), we adopt the quaternion FT (QFT) for conducting multiscale and multifeature processing in a parallel way, to obtain the frequency domain representation of the hyperspectral images (HSIs). This helps with robust detection performance. Experimental results on four real HSIs validate the remarkable detection performance and excellent time efficiency of our proposed approach when compared to some state-of-the-art anomaly detection methods.

Two 3D Fractal-Based Approaches for Topographical Characterization: Richardson Patchwork versus Sdr.

Various methods exist for multiscale characterization of surface topographies, each offering unique insights and applications. The study focuses on fractal-based approaches, distinguishing themselves by leveraging fractals to analyze surface complexity. Specifically, the Richardson Patchwork method, used in the ASME B46.1 and ISO 25178 standards, is compared to the Sdr parameter derived from ISO 25178-2, with a low-pass Gaussian filter for multiscale characterization. The comparison is performed from the relative area calculated on topographies of TA6V samples grit blasted with different pressures and blasting materials (media). The surfaces obtained by grit blasting have fractal-like characteristics over the scales studied, enabling the analysis of area development at multiple levels based on pressure and media. The relative area is similar for both methods, regardless of the complexity of the topographies. The relevance scale for each calculation method that significantly represents the effect of grit blasting pressure on the increased value of the relative area is a tiling of 7657.64 µm² of triangle area for the Patchwork method and a 124.6 µm cut-off for the low-pass Gaussian filter of the Sdr method. These results could facilitate a standard, friendly, new fractal method for multiscale characterization of the relative area.

An intelligent lithology recognition system for continental shale by using digital coring images and convolutional neural networks

Lithology identification is a critical component of reservoir evaluation and hydrocarbon development. However, the continental shale rocks, characterized by complex lithologies and developed laminations, present new challenges to traditional lithology identification techniques. In this study, we develop a centimeter-level intelligent identification system for continental shale lithology, leveraging digital image preprocessing and deep learning methods. Specifically, we construct a dataset from 400 m of drilling cores, encompassing 24 types of lithology and 152 lithological transitions. The downsampling and oversampling methods are employed to balance the dataset. The digital image denoising and restoration techniques are then utilized to enhance rock image features. Different neural network models are examined to obtain the optimum predictive results. The results indicate that data balancing improves the TOP1 and TOP5 accuracies by 10% and 5%, respectively. Gaussian low-pass filter denoising, in comparison to other methods, increases the TOP1 and TOP5 accuracies by 2%, establishing it as the superior denoising technique for lithology recognition. Considering both computational efficiency and prediction accuracy, EfficientNet emerges as the most effective model for continental shale image recognition. The optimized model attains up to 60% TOP1 and 95% TOP5 across 22 categories in drilling core samples. This study delves into the intelligent identification of macroscopic drilling core images of continental shale, and the findings can offer technical support for the interpretation and evaluation of continental shale reservoirs.

Performance analysis of GFSK modulation over AWGN channel

This paper analyses the performance of Gaussian Frequency Shift Keying (GFSK) modulation in Additive White Gaussian Noise (AWGN) channels. Using MATLAB 2022b for simulations, the study explores GFSK modulation principles, the role of Gaussian low-pass filters, the characteristics reflected by Power Spectral Density (PSD), and the influence of Bit Time-Bandwidth Product (BT) on Bit Error Rate (BER). It has been observed that the Gaussian filter restricts sidelobe amplitudes and enhances spectral efficiency. Through varying BT values, it is observed that higher BT values correlate with lower BER. Additionally, the study successfully reconstructs original baseband signals through sampling decisions, confirming GFSK modulation effectiveness.

Efficient Performance Analysis of Image Enhancement Filtering Methods Using MATLAB

Image enhancement is both an art and a science, playing a pivotal role in enhancing the quality of high-resolution images like those captured by digital cameras. Its primary goal is to unveil hidden details within an image and augment the contrast in images with low contrast. This method offers a plethora of options for elevating the visual appeal of images, making it an indispensable tool in numerous applications that face challenges such as noise reduction, degradation, and blurring. In this paper, we implemented frequency domain low pass filters like ideal low pass filter, Butterworth low pass filter and Gaussian low pass filters with execution time using MATLAB. The Butterworth low pass filter given better results than other two with less execution time.

Error-diffusion-kernel parameters for binary pattern in 1-bit fringe projection profilometry.

In fringe projection profilometry, 1-bit processing of 8-bit raster patterns is a common method to suppress nonlinear errors in commercial projectors and realize high-speed projection in industrial projectors. In the process of generating 1-bit fringes from sinusoidal fringes, the generation of high-order harmonics is inevitable; choosing to introduce fewer high-order harmonics of the algorithm is conducive to defocus to obtain a better sinusoidal pattern. This paper proposes a method to expand the error-diffusion kernel of the conventional Floyd-Steinberg diffusion dithering algorithm from 2×3 to 3×5, which can reduce the grayscale change of surrounding pixels and generate 1-bit fringes with fewer high-order harmonics. Meanwhile, this paper optimizes the parameters of the 3×5 error-diffusion kernel and proposes the optimal parameters for this kind of diffusion kernel. The simulation results show that the fringes generated by the proposed 3×5 error-diffusion-kernel algorithms are closer to sinusoidal fringes after Gaussian low-pass filtering. The experimental results show that the accuracy of the 3×5 diffusion kernel algorithms is higher.

Local refinement mechanism for improved plant leaf segmentation in cluttered backgrounds.

Plant phenotyping is a critical field in agriculture, aiming to understand crop growth under specific conditions. Recent research uses images to describe plant characteristics by detecting visual information within organs such as leaves, flowers, stems, and fruits. However, processing data in real field conditions, with challenges such as image blurring and occlusion, requires improvement. This paper proposes a deep learning-based approach for leaf instance segmentation with a local refinement mechanism to enhance performance in cluttered backgrounds. The refinement mechanism employs Gaussian low-pass and High-boost filters to enhance target instances and can be applied to the training or testing dataset. An instance segmentation architecture generates segmented masks and detected areas, facilitating the derivation of phenotypic information, such as leaf count and size. Experimental results on a tomato leaf dataset demonstrate the system's accuracy in segmenting target leaves despite complex backgrounds. The investigation of the refinement mechanism with different kernel sizes reveals that larger kernel sizes benefit the system's ability to generate more leaf instances when using a High-boost filter, while prediction performance decays with larger Gaussian low-pass filter kernel sizes. This research addresses challenges in real greenhouse scenarios and enables automatic recognition of phenotypic data for smart agriculture. The proposed approach has the potential to enhance agricultural practices, ultimately leading to improved crop yields and productivity.

Preliminary Analysis on the Effect of Different Denoising Techniques towards Texture Features of MRI Images of Alzheimer’s Disease

Early detection of Alzheimer’s disease (AD) has become one of the major research topics nowadays. The utilization of the computerized system may help medical experts to better understand and analyse the magnetic resonance imaging (MRI) images of AD patients for early detection. One of the commonly steps taken for the analysis of the image is image denoising using certain filters. However, finding shows that previous researchers use different approaches. This study aims to analyse the effect of different denoising techniques towards detection of Alzheimer’s disease. Data of two different groups (AD patients and Normal Control) were collected from Alzheimer's Disease Neuroimaging Initiative (ADNI). Then brain extraction and skull stripping were performed. Several image denoising techniques were implemented for both groups namely median filter, Wiener filter, histogram equalization filter and Gaussian low pass filter. After that, all images underwent texture feature extraction process and analysis were made to see the effect of those denoising techniques towards the features of Gray-Level Co-occurrence Matrix (GLCM) extracted which are the contract, correlation, energy and homogeneity features. The result shows that the use of mentioned denoising filters do not give effect to the extracted features. All values of contrast, correlation, energy and homogeneity cannot clearly distinguish between AD and NC groups. Without any filters on the other hand, contrast feature gives the best output in distinguishing between AD and NC groups with the normalized value of 0.1. The result from this study may help in thorough investigation of other features or hybrid features that could be used for the purpose of detection and classification of AD.

Qualitative interpretation of high resolution aeromagnetic data of abeokuta metropolis for geological characterisation

Qualitative interpretation of aeromagnetic maps involves the visual inspection and pattern analyses of produced maps to identify potential geological structures in the magnetic field by processing the dataset to characterize the Earth's magnetic variations and map subsurface geological structure which is important to tackle the challenges of resource management, environmental assessment and infrastructure design in the study area. This technique has been employed to a part of Abeokuta metropolis of Ogun state, Southwestern Nigeria, using high resolution aeromagnetic dataset acquired from the Nigerian Geological Survey Agency (NGSA). Pre-processing analyses such as data transformation, map re-projection and power spectrum plot were performed on the dataset to minimize noises and remove unwanted variations. The application of Reduction to Magnetic Equator (RTE) filter was to remove asymmetries in the data and position the peaks of magnetic anomalies over their sources while the Gaussian (low pass) filter was aimed at improving the signal-to-noise ratio. Low magnetic zones indicated by blue colours from the total magnetic fields possibly reveal the sedimentary intrusions into the area, intermediate magnetic regions having green, yellow and red colourations suggest transition zones and the Northeastern portion of the maps indicated with pink colours correlate to the core basement regions. Derivative filters are applied as enhancement techniques to accentuate the inferred linear subsurface structures interpreted as cracks, fractures, lineaments, etc. These structures are predicted mineral entrapments or fault lines whose NE-SW orientation conforms to the Pan African Orogeny and they can aid environmental assessments generally.

Quantification of soot nanostructure produced from a diesel engine fueled with C3 ketone

The soot emission is an urgent problem that deserves extensive studies to suppress it. The smallest ketone was investigated in this study as a soot suppressor in a diesel engine. The fringe analysis approach was used to quantify the nanostructure characteristics of soot. At first, the image processing parameters were updated for this study, and their effects were explored and interpreted. Image resolution was found to be a crucial parameter in the image analysis process with a spatial resolution of 0.0417 nm/pixel as the best adjustment for the present study. The contrast-limited adaptive histogram equalization approach provided a perfect histogram equalization. A Gaussian low-pass filter with a standard deviation of 1.5 and a filter size of 11 produced the best results. The bottom hat transformation with a structuring element of a five pixels size and zero corners produced the best fringes' representation. A threshold level of 0.8 as Otsu's level provided a better nanostructure's representation. Second, the images of soot from acetone/diesel blends were analyzed using the updated parameters. The addition of acetone reduced the formation of soot in a diesel engine. The fringe length, tortuosity, intensity and alignment were all decreased, while fringe spacing was increased with acetone.

Implementation of 23 Gbps optical wireless link for 750 km inter-aircraft communications

Optical device research and development is crucial for the implementation of miniaturized wireless optical transmission systems that are capable of transmitting large amounts of data at fast speeds. This work presents the design and evaluation of an optical link for the inter-aircraft interconnects. At the transmitter, the vertically cavity surface emission laser (VCSEL) is used to generate a directly modulated non-return to zero (NRZ) signal. In the receiver, direct detection and Gaussian low-pass filtering are used. The system’s performance is evaluated and compared with and without the use of filtration at the detector. BER, sensitivities, and data transmission capability are used to evaluate the performance of optical wireless systems using optisystem software. The range and data rate are enhanced 9-fold and 4-fold, respectively, in comparison to earlier schemes. The results presented show a transmission range of 750 km at a 23 Gbps data rate with a BER of 10−29.

Mach–Zehnder electro-optic modulators chirping management in high speed data communication lightwave systems

Abstract This work demonstrates Mach–Zehnder electro-optic modulators chirping management in high speed data communication lightwave systems. The link with applied voltage on the modulators arms is clarified. We have analyzed the chirp in MZ lithium niobate modulators output. The resulted signal passed on the channel that consists of the loop control that we used not to lose too much power because we use an amplifier after a period of fiber reach cable. The resulted signal distance depends on the number of loops multiplied by the fiber reach cable. The next step is to receive the signal by photodetector pin with a responsivity of 1 A/W. The signal after that passed on low pass Gaussian filter with a cut of bandwidth of 0.75 × Bit rate.

Implementation of 10 Gbps optical wireless link for 200 km inter-aircraft optical communications

Abstract The enhancements related to optical devices are highly required for high-speed compact wireless optical transmission systems. This paper focuses on development and analysis of the communication system for inter-aircraft applications. The vertical cavity surface emitting laser (VCSEL) is employed at the transmitter for generating directly modulated non-return to zero (NRZ) signal. Direct detection along with Gaussian low pass filtering is employed in the receiver. The performance of the system is examined as well as compared with and without application of filtering at the receiver. The performance of optical wireless system is analyzed in terms of BER, eye-pattern, sensitivity, SNR, and Q-factor. The data rate is enhanced by 4-fold and transmission distance is improved by 250% in comparison with earlier inter-satellite links.

Electro-optic power/signal quality optimization based on the interaction between electro-optic modulators and electrical filters

Abstract This paper clarifies the effects of electrical filters with electro-optic modulators variations on the power/signal quality optimization and minimum data error rates reduction. The electro-optic modulators that are namely Mach Zehnder modulator, absorption modulator measured, single-drive Mach Zehnder modulator measured, and electro-absorption modulator. In addition to the filters are employed low pass (LP) Gaussian filter, LP Bessel filter, LP Butterworth filter, and LP Chebyshev filter. The hybrid combination between these modulators at the Tx. and the filters at the Rx. side give the chance to set the best candidate combinations from these devices for the power and signal quality optimization.

Low-pass U-Net: a segmentation method to improve strip steel defect detection

The detection of strip steel surface defects is critical to ensuring the quality of strip steel products. Many deep learning-based methods have been presented and can achieve outstanding performance. However, most of these methods ignore the frequency information among defect areas, which plays an important role in defect detection. This paper proposes a deep learning method to further improve defect segmentation effects based on existing methods, called low-pass U-Net. Since most defects in strip steel are located in high-frequency areas, we implement a low-pass filter before downsampling in the encoder, which prevents aliasing and separates out high-frequency information. The high-frequency feature is transferred into the decoder to assist segmentation. Following previous studies, we propose an adaptive variance Gaussian low-pass layer to generate different filters according to each spatial location of the feature map, with lower computing resource use. Furthermore, to detect defects at significantly different scales, an improved Hypercolumn module is adopted at the end of the decoder to upsample and fuse the feature maps in different resolutions, where Subpixel replaces the bilinear interpolation to refine the upsampled results. The proposed method is validated on practical datasets and achieves considerable performance improvement (with a best Dice coefficient of 0.903), which demonstrates the effectiveness of low-pass U-Net. The introduction of the adaptive variance Gaussian low-pass filter layer results in a 3% increase in Dice coefficient in a comparative inference time, which achieves a balance in performance, inference time and complexity.

A frequency-limited waviness and curvature measurement algorithm for composite fibre trackings

Given a set of irregularly sampled 3D polygonal curves representing composite fibres within a micro-computed tomography volume, a new approach based on the Frenet–Serret formulas is proposed to measure the point curvature and waviness along a polyline even when its oscillations are not coplanar. However, a direct computation of the measures would lead to ill-formed results depending on variant externalities across acquisitions such as noise, sampling, resolution, fractality, etc. Consequentially, we also propose a decoupling mechanism employing a low-pass Gaussian frequency filter to gradually discard features smaller than a certain user-specified σ wavelength referenced in actual space units. This proposal has been tested, characterized and visualized using both real and synthetic datasets contemplating complex waveform features to assess the filter selectivity and convergence across varying sampling frequencies (i.e. polyline resolution). The C++ VTK implementation, alongside an extra amount of supplementary materials encompassing the execution results and synthetic datasets is provided.

Photometric Stereo-Based 3D Reconstruction Method for the Objective Evaluation of Fabric Pilling

Fabric pilling evaluation has been considered as an essential element for textile quality inspection. Traditional manual method is still based on human eyes and brain, which is subjective with low efficiency. This paper proposes an objective evaluation method based on semi-calibrated near-light Photometric Stereo (PS). Fabric images are digitalized by self-developed image acquisition system. The 3D depth information of each point could be obtained by PS algorithm and then mapped to 2D grayscale image. After that, the non-textured image could be filtered by using the Gaussian low-pass filter. The pilling segmentation is conducted by using global iterative threshold segmentation method, and then K-Nearest Neighbor (KNN) is finally selected as a tool for the grade classification of fabric pilling. Our experimental results show that the proposed evaluation system could achieve excellent judging performance for the objective pilling evaluation.

Robust, imperceptible and optimized watermarking of DICOM image using Schur decomposition, LWT-DCT-SVD and its authentication using SURF.

In this proposed work, a dual image watermarking algorithm is used to protect the data against copyright violations. In this work, the DICOM image is used as a host image. Two watermark images used are the MNNIT logo and the personal data of the patient. This method utilizes the advantages of Schur decomposition, lifting wavelet transform (LWT), discrete cosine transform (DCT) and singular value decomposition (SVD). The scaling factor is a vital parameter of watermarking technique. The firefly optimization technique is used to get the optimized scaling factor. The Speeded-up robust features (SURF) are used for watermarking authentication. To evaluate the performance of the proposed algorithm, peak signal-to-noise ratio (PSNR), normalized correlation coefficient (NCC), and structural similarity index measurement (SSIM) are used. The proposed method is tested against various attacks such as Salt and Pepper noise, Gaussian noise, Gaussian low pass filter, Average filter, Median filter, Histogram equalization, Sharpening, Rotation and Region of interest filtering. The proposed algorithm shows a high level of robustness and imperceptibility. It is found that the features of the input host image and the watermarked image are matching correctly on applying the SURF technique.

Performance of QR Code Detectors near Nyquist Limits.

For the interacting with real world, augmented reality devices need lightweight yet reliable methods for recognition and identification of physical objects. In that regard, promising possibilities are offered by supporting computer vision with 2D barcode tags. These tags, as high contrast and visually well-defined objects, can be used for finding fiducial points in the space or to identify physical items. Currently, QR code readers have certain demands towards the size and visibility of the codes. However, the increase of resolution of built-in cameras makes it possible to identify smaller QR codes in the scene. On the other hand, growing resolutions cause the increase to the computational effort of tag location. Therefore, resolution reduction in decoders is a common trade-off between processing time and recognition capabilities. In this article, we propose the simulation method of QR codes scanning near limits that stem from Shannon’s theorem. We analyze the efficiency of three publicly available decoders versus different size-to-sampling ratios (scales) and MTF characteristics of the image capture subsystem. The MTF we used is based on the characteristics of real devices, and it was modeled using Gaussian low-pass filtering. We tested two tasks—decoding and locating-and-decoding. The findings of the work are several-fold. Among others, we identified that, for practical decoding, the QR-code module should be no smaller than 3–3.5 pixels, regardless of MTF characteristics. We confirmed the superiority of Zbar in practical tasks and the worst recognition capabilities of OpenCV. On the other hand, we identified that, for borderline cases, or even below Nyquist limit where the other decoders fail, OpenCV is still capable of decoding some information.

Preprocessor Rate Control for Adaptive Multi-View Live Video Streaming Using a Single Encoder

Currently, an increasing number of technical systems are equipped with multiple cameras. Limited by cost and size, they are often restricted to a single hardware encoder. The combination of all views into a single superframe allows for streaming all camera views at the same time, but it prevents individual rate/quality adaptations on those camera views. We propose a preprocessing filter concept that allows for individual rate/quality adaptation while using a single encoder. Additionally, we create a preprocessor model that estimates the required preprocessing filter parameters from the specified encoding parameters. This means our approach can be used with any existing multi-view adaptation scheme designed for controlling multiple encoders. We design both an analytical and a Machine Learning-based bitrate model. Because both models perform equally well, we suggest using either one as the core part of our preprocessor model. Both models are specifically designed for estimating the influence of the quantization parameter, frame rate, frame size, group of pictures length, and a Gaussian low-pass filter on the video bitrate. Furthermore, the rate models outperform state-of-the-art bitrate models by at least 22% regarding the overall root mean square error. Our bitrate models are the first of their kind to consider the influence of a Gaussian low-pass filter. We evaluate the preprocessing approach by streaming six camera views in a teledriving scenario with a single encoder and compare it to using six individual encoders. The experimental results demonstrate that the preprocessing approach achieves bitrates similar to the individual encoders for all views. While achieving a comparable rate and quality for the most important views, our approach requires a total bitrate that is 50% smaller than when using a single encoder approach without preprocessing.