scholarly journals A New Adaptive Spatial Filtering Method in the Wavelet Domain for Medical Images

2020 ◽  
Vol 10 (16) ◽  
pp. 5693
Author(s):  
Maria Simona Răboacă ◽  
Cătălin Dumitrescu ◽  
Constantin Filote ◽  
Ioana Manta
Keyword(s):  
Spatial Filtering ◽  
Threshold Value ◽  
Spatial Filter ◽  
Wavelet Domain ◽  
Wavelet Coefficients ◽  
Local Statistics ◽  
Filtering Method ◽  
New Methods ◽  
Computational Resources ◽  
Adaptive Spatial Filter

Although there are many methods in the literature to eliminate noise from images, finding new methods remains a challenge in the field and, despite the complexity of existing methods, many of the methods do not reach a sufficient level of applicability, most often due to the relatively high calculation time. In addition, most existing methods perform well when the processed image is adapted to the algorithm, but otherwise fail or results in significant artifacts. The context of eliminating noise from images is similar to that of improving images and for this reason some notions necessary to understand the proposed method will be repeated. An adaptive spatial filter in the wavelet domain is proposed by soft truncation of the wavelet coefficients with threshold value adapted to the local statistics of the image and correction based on the hierarchical correlation map. The filter exploits, in a new way, both the inter-band and the bandwidth dependence of the wavelet coefficients, considering the minimization of computational resources.

Velocity Measurement for Flow Particles by Using Spatial Filtering Technique Based on Sensor Array

2012 ◽  
Vol 508 ◽  
pp. 263-266 ◽  
Author(s):  
Zeng Xi Lu ◽  
Gang Yang ◽  
Zeng Shuang Wang
Keyword(s):  
Particle Velocity ◽  
Velocity Measurement ◽  
Sensor Array ◽  
Spatial Filtering ◽  
Spatial Filter ◽  
Capacitance Sensor ◽  
Filtering Method ◽  
Filtering Technique ◽  
Moving Particle ◽  
Particle Velocity Measurement

In comparison with cross correlative method based particle velocity measurement, the spatial filtering method for particle velocity measurement has the advantages of simplicity of the measurement system and convenience of data processing. In the paper a capacitive velocimeter, based on the spatial filtering method, is described. In this approach a capacitance sensor array has been used as a spatial filter for particle velocity measurements. Experiments were performed on a test rig and the experimental results show that the system relative error is within ±4% over a velocity range of 2-5m/s for a moving particle.

Spatial filtering of signals with spectrum overlapping

2019 ◽  
pp. 27-33
Author(s):  
M. E. Shevchenko ◽  
A. V. Gorovoy ◽  
S. N. Solovyov
Keyword(s):  
Antenna Arrays ◽  
A Priori ◽  
Spatial Filtering ◽  
Spatial Filter ◽  
Radio Sources ◽  
Probability Of Error ◽  
Shift Keying ◽  
A Priori Uncertainty

The paper considers the spatial filtering methods of signals with spectrum overlapping under conditions of a priori uncertainty of the directions of arrival from radio sources. The estimates of the directions of signals arrival obtained by ESPRIT or MUSIC are used in order to build a spatial filter. It is shown that when using ESPRIT, unlike MUSIC, an additional calculations of filter coefficients based on estimates of the directions of signals arrival are not required, and the quadrature components of the signals are formed simultaneously with estimates of the direction of their arrival. The probability of error performances of minimum shift keying signals which were divided by spatial filtering on the basis of ESPRIT and MUSIC using seven-element circular and angular antenna arrays are given.

Steganographic Method with High Visual Concealment in Wavelet Domain

2013 ◽  
Vol 281 ◽  
pp. 47-50
Author(s):  
Zhi Hong Chen
Keyword(s):  
Wavelet Transformation ◽  
Experimental Results ◽  
Wavelet Domain ◽  
Wavelet Coefficients ◽  
Local Neighborhood ◽  
The Impact

In this paper we propose a new steganographic method, which based on wet paper codes and wavelet transformation. The method is designed to embed secret messages in images' wavelet coefficients and depends on images' texture characters in local neighborhood. The receivers can extract secret bits from carrier images only by some matrix multiplications without knowing the formulas written by senders, which further improves steganographic security and minimizes the impact of embedding changes. The experimental results show that our proposed method has good robust and visual concealment performance and proves out it's a practical steganographic algorithm.

scholarly journals An adaptive wavelet transformation filtering algorithm for improving road anomaly detection and characterization in vehicular technology

2019 ◽  
Vol 9 (5) ◽  
pp. 3664
Author(s):  
Habeeb Bello-Salau ◽  
A. J. Onumanyi ◽  
B. O. Sadiq ◽  
H. Ohize ◽  
A. T. Salawudeen ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
False Alarm ◽  
Wavelet Transformation ◽  
Multiple Scales ◽  
Detection System ◽  
Spatial Filter ◽  
Adaptive Threshold ◽  
Wavelet Coefficients ◽  
Adaptive Wavelet ◽  
Vehicular Technology

Accelerometers are widely used in modern vehicular technologies to automatically detect and characterize road anomalies such as potholes and bumps. However, measurements from an accelerometer are usually plagued by high noise levels, which typically increase the false alarm and misdetection rates of an anomaly detection system. To address this problem, we have developed in this paper an adaptive threshold estimation technique to filter accelerometer measurements effectively to improve road anomaly detection and characterization in vehicular technologies. Our algorithm decomposes the output signal of an accelerometer into multiple scales using wavelet transformation (WT). Then, it correlates the wavelet coefficients across adjacent scales and classifies them using a newly proposed adaptive threshold technique. Furthermore, our algorithm uses a spatial filter to smoothen further the correlated coefficients before using these coefficients to detect road anomalies. Our algorithm then characterizes the detected road anomalies using two unique features obtained from the filtered wavelet coefficients to differentiate potholes from bumps. The findings from several comparative tests suggest that our algorithm successfully detects and characterizes road anomalies with high levels of accuracy, precision and low false alarm rates as compared to other known methods.

scholarly journals Noise Reduction Algorithm based on Spatial Filtering in the Wavelet Domain

2020 ◽  
Author(s):  
Maria Simona Răboacă ◽  
Cătălin Dumitrescu ◽  
Constantin Filote ◽  
Ioana Manta
Keyword(s):  
Noise Reduction ◽  
Spatial Filtering ◽  
Reduction Algorithm ◽  
Wavelet Domain ◽  
Noise Reduction Algorithm

The spatial filtering method for solid particle velocity measurement based on an electrostatic sensor

2009 ◽  
Vol 20 (4) ◽  
pp. 045404 ◽  
Author(s):  
Chuanlong Xu ◽  
Guanghua Tang ◽  
Bin Zhou ◽  
Shimin Wang
Keyword(s):  
Solid Particle ◽  
Particle Velocity ◽  
Velocity Measurement ◽  
Spatial Filtering ◽  
Filtering Method ◽  
Particle Velocity Measurement

scholarly journals A Robust Spatial Filtering Method for DOA Estimation of Coherent Sources in Impulsive Interference

1999 ◽  
pp. 46-56
Author(s):  
Marilli Rupi ◽  
Panagiotis Tsakalides ◽  
Enrico Del Re ◽  
Chrysostomos L. Nikias
Keyword(s):  
Spatial Filtering ◽  
Doa Estimation ◽  
Filtering Method ◽  
Impulsive Interference ◽  
Coherent Sources

Land cover pattern simulation using an eigenvector spatial filtering method in Hubei Province

2020 ◽  
Vol 13 (4) ◽  
pp. 989-1004
Author(s):  
Jiaxin Yang ◽  
Yumin Chen ◽  
John P. Wilson ◽  
Huangyuan Tan ◽  
Jiping Cao ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Filtering ◽  
Hubei Province ◽  
Filtering Method ◽  
Eigenvector Spatial Filtering ◽  
Pattern Simulation ◽  
Cover Pattern

scholarly journals Spatial Filtering in DCT Domain-Based Frameworks for Hyperspectral Imagery Classification

2019 ◽  
Vol 11 (12) ◽  
pp. 1405 ◽  
Author(s):  
Razika Bazine ◽  
Huayi Wu ◽  
Kamel Boukhechba
Keyword(s):  
Hyperspectral Imagery ◽  
Wiener Filter ◽  
Spatial Filtering ◽  
Spatial Filter ◽  
Computational Time ◽  
Support Vector ◽  
Two Dimensional ◽  
Significant Information ◽  
Dct Domain ◽  
Frequency Components

In this article, we propose two effective frameworks for hyperspectral imagery classification based on spatial filtering in Discrete Cosine Transform (DCT) domain. In the proposed approaches, spectral DCT is performed on the hyperspectral image to obtain a spectral profile representation, where the most significant information in the transform domain is concentrated in a few low-frequency components. The high-frequency components that generally represent noisy data are further processed using a spatial filter to extract the remaining useful information. For the spatial filtering step, both two-dimensional DCT (2D-DCT) and two-dimensional adaptive Wiener filter (2D-AWF) are explored. After performing the spatial filter, an inverse spectral DCT is applied on all transformed bands including the filtered bands to obtain the final preprocessed hyperspectral data, which is subsequently fed into a linear Support Vector Machine (SVM) classifier. Experimental results using three hyperspectral datasets show that the proposed framework Cascade Spectral DCT Spatial Wiener Filter (CDCT-WF_SVM) outperforms several state-of-the-art methods in terms of classification accuracy, the sensitivity regarding different sizes of the training samples, and computational time.

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