Median Filtering Algorithm Research Articles

Improved approximated median filter algorithm for real-time computer vision applications

Median filter is one of the predominant filters that are used to suppress impulse noise. Its simplicity and ability to maintain edges has led to an extensive application in the domain of image processing and computer vision. However, challenges such as moderate to high running time of the standard median filter algorithm and relatively poorer performance when the image is highly corrupted with impulse noise, have led to the design of several variations of the algorithm. One set of variation of the algorithm concentrates on generating quality outputs, while the other set focuses on reducing running time. Among the set targeting the reduction of the running time of the median filter is the DP approximated median filter. However, DP performs poorly when images are corrupted with moderate to high levels of noise. This paper therefore proposes an Improved Approximation Median Filtering Algorithms (IAMFA-I & IAMFA-II) based on DP to generate a better output. The introduction of Mid-Value-Decision-Median in DP reduces the chances of selecting corrupted pixel for denoised image. Experimental results indicate that the IAMFA-II has better running time and equivalent output compared with DP, while IAMFA-I generates better output and has equivalent running time when compared with DP.

Baseline correction algorithm for Raman spectra based on median filtering and un-uniform B-spline

As one of the key steps for data preprocessing of Raman spectra, baseline correction is an effective method to eliminate fluorescence interference. Traditional algorithms such as polynomial fitting and uniform B-spline fitting are simple and easy to implement, but the uncertain fitting order and internal knots limit their flexibility. In addition, the baseline correction results of traditional algorithms often occur over and under fitting phenomena. Therefore, we propose a baseline correction algorithm for Raman spectra based on median filtering and un-uniform B-spline. Firstly, the trough points of the spectral data are filtered by smoothing preprocess, difference calculation and threshold setting, and the internal knots of the un-uniform B-spline are adaptively selected by the trough positions of the spectral data. Then, the median filtering algorithm is used to process the spectral data so that the un-uniform B-spline has a better baseline fitting effect at the position where the signal changes from peak to smooth band. Finally, the un-uniform B-splines is used to fit the baseline by fitting the baseline iteratively. The proposed algorithm overcomes the shortcoming of traditional B-spline algorithm that the internal knots need to be selected manually based on different Raman spectra, and also avoids influencing the baseline fitting by random noise in the spectral data, and thus further improving the spectral baseline correction effect. The original Raman spectra of polymethyl methacrylate and normal octane are used for experimentally evaluating the baseline correction effect. Compared with the results from polynomial fitting, uniform B-spline and adaptive iteratively reweighted penalized least squares algorithms, the experimental results show that the proposed algorithm can well eliminate the Raman signal baseline drift effectively without over or under fitting phenomena, and it can perform better baseline correction for different baseline drift situations. Therefore, the proposed algorithm can provide more accurate and reliable information for the further analysis of spectral data.

Contaminated Facade Identification Using Convolutional Neural Network and Image Processing

In recent years, as number of new building getting larger, there has been an increased interest in the cleaning of exterior walls. Accordingly, there is a growing interest in automatic cleaning robots that move around the outer building facade. These robots are also required to apply different cleaning methods to remove various contaminants on the outer wall of the building. However, current surface contaminant detection systems can either detect only a single type of contaminant, or are not compact enough for installation on mobile platforms that move around the outer facade. As cleaning workers are able to distinguish various contaminants with the naked eye, we aim to solve this problem by developing a machine-vision system using convolutional neural networks (CNNs) and image processing methods. As it is a compact system that uses only a camera to take pictures and a processor to process the images, it is suitable for applications involving mobile platforms. Object-type contaminants such as avian feces are handled by the YOLOv3 module using the object-detection algorithm. Area-type contaminants such as rusty stains are processed using the color-detection module using the HSV color space, median filter, and flood fill algorithm. Particle-type contaminants such as dust are handled by the grayscale module, converting images to grayscale images and then comparing the average brightness with a reference that is provided in advance. This proposed machine vision system will detect objects, areas, and particle-type contaminants with a single image and some reference images provided in advance.

Ensemble Heterogeneous Feature Selection (EHFS) and Heterogeneous Ensemble Classifier with VOTE (HECV) for Automatic Detection of Snore Sounds

Obstructive Sleep Apnea (OSA) is generally considered as a sleep co-related breathing difficulty with some important well known disabling indication. This research work tends to differentiate the condition of OSA victims. The significant work is to propose an innovative Heterogeneous Ensemble Classifier with Velum Oropharyngeal Tongue Epiglottis (HECV) method with a high level of effectiveness. In the formulated study, data recording, features extraction, multifeature selection, classification and performance evaluation are the five stages of processing. At the initial stage, the noises represented in the audio signals which were eliminated through Adaptive Fuzzy Median Filter (AFMF) algorithm. After thatCrest Factor, original Frequency, Spectral Frequency Features, Subband Energy Ratio, Mel-Scale Frequency Cepstral Coefficients (MFCC), Empirical Mode Decomposition (EMD) Features, and Wavelet Energy Features are collected from the noise suppressed audio signals and inputs are fed into Ensemble Heterogeneous Feature Selection (EHFS) technique. EHFS algorithm fuses the outputs of filter and wrapper oriented feature selection methods. These identified features are classified using HECV technique which renders a good classification outputs by validation without the regard of subjects. The outputs show that the formulated HECV approach gives better performance in snore detection when co-related with other classifiers.

Grayscale Image Quality Analysis Result of Noises Reduction using Adaptive Fuzzy Filter (AFF) and Spatial Median Filter (SMF) Against Image Depth Variations

Removing salt and pepper noise in the image will have an impact on information in the image, with a greater percentage of noise, the changes in the results image will also be large, this is very likely different from the types of grayscale images with different depths such as 8, 16 and 24 bit. The percentage of noise that will be used starts from 45%, this is based on previous studies which reveal the ability of noise reduction algorithms to be able to work a maximum of below 20%, so that it needs to be studied more deeply the performance of the algorithm and its impact on images with greater noise. From the results of previous research studies, the Spatial Median Filter algorithm and Adaptive Fuzzy Filter algorithm can reduce noise with the maximum percentage of noise below 45% in the 8 Bit image, but leave some noise after being reduced. The test results on grayscale image obtained the average overall results quality Adaptive Fuzzy Filter algorithm is better than Spatial Median Filter in terms of image quality using PSNR but the diversity of information is even more reduced compared to the SMF algorithm referring to the entropy shannon value.

A Segmentation Perspective of Non-Cooperative Iris Recognition

Iris Segmentation, an initial and vital stage of the iris recognition stage which directly affects the recognition accuracy. Especially, the non-cooperative environment that leads to contain many of the noise parameters in the captured image. Since the recognition accuracy of the iris biometrics system is extremely dependent on the proper iris segmentation, this paper is devoted to the segmentation perspective of the non-cooperative iris recognition system. The initial stage of the proposed method is started with applying a hybrid median filter algorithm to remove the possible noises and then a region-based level set algorithm is applied to overcome the identification of the concave property in the non-cooperative iris segmentation and enhanced Otsu’s thresholding method is applied to the pupil segmentation. UBIRIS, a publicly available iris database for the non-cooperative situation, Version 1 and Version 2 is used for the implementation purpose. The accuracy of the segmentation result is achieved as 94.56 and 94.53 for the UBIRISv.1 and UBIRISv.2 respectively which show the proposed method as a better one.

Research on Weld Quality Detection Method Based on Machine Vision and Computer Image Processing

In the weld quality inspection system based on machine vision and computer image processing, according to the principle of laser triangulation, a line laser is used to project structured light on the weld surface, and the weldment moves uniformly in a straight line on the moving platform. At the same time, the CMOS camera captures the structured light image and transmits it to the industrial computer through Gigabit Ethernet. The image is denoised by the median filtering algorithm on the industrial computer, then the image is segmented by the maximum inter-class error (Otus) method, and the image is binarized according to the threshold value. Then the center of the stripe is extracted by constructing four direction templates, and the 3D model is reconstructed by curve fitting. After the completion of the image processing, the geometric size of the weld is measured by mathematical formula. After the weld seam measurement is completed, the weld seam is transversely cut off at the same position, measured by manual method, and then the data measured by the two methods are compared. Through repeated measurements and comparisons of weld width, residual height and back forming, the precision of the system meets the practical requirements. The weld forming varies with the welding parameters within a certain range, but changes little and has good stability.

Super-Resolution Reconstruction of Cell Pseudo-Color Image Based on Raman Technology.

Raman spectroscopy visualization is a challenging task due to the interference of complex background noise and the number of selected measurement points. In this paper, a super-resolution image reconstruction algorithm for Raman spectroscopy is studied to convert raw Raman data into pseudo-color super-resolution imaging. Firstly, the Raman spectrum data of a single measurement point is measured multiple times to calculate the mean value to remove the random background noise, and innovatively introduce the Retinex algorithm and the median filtering algorithm which improve the signal-to-noise ratio. The novel method of using deep neural network performs a super-resolution reconstruction operation on the gray image. An adaptive guided filter that automatically adjusts the filter radius and penalty factor is proposed to highlight the contour of the cell, and the super-resolution reconstruction of the pseudo-color image of the Raman spectrum is realized. The average signal-to-noise ratio of the reconstructed pseudo-color image sub-band reaches 14.29 db, and the average value of information entropy reaches 4.30 db. The results show that the Raman-based cell pseudo-color image super-resolution reconstruction algorithm is an effective tool to effectively remove noise and high-resolution visualization. The contrast experiments show that the pseudo-color image Kullback–Leiber (KL) entropy of the color image obtained by the method is small, the boundary is obvious, and the noise is small, which provide technical support for the development of sophisticated single-cell imaging Raman spectroscopy instruments.

An improved adaptive weighted median filter algorithm

An improved adaptive weighted median filtering method is proposed to deal with the interference noise of ultrasonic RF signal. Firstly, edge pixel points are determined to be filtered by the method of extending edge points; secondly, mean value is used to replace the median value which considered to be noise points; finally, weighted smoothing processing is carried out. The final experimental results in this paper show that the proposed method has better effect on RF signal processing.

Foggy image–sharpening method with multi-channel polarization information system

This article aims to reduce the influence of heavy fog on the outdoor imaging equipment and to maximally improve the foggy image resolution. It is known that reflected light of the object includes the abundant target polarization information. The foggy image can be restored by these information of the target and the airlight. Therefore, this article introduces a multi-channel polarization information system and defogging algorithm accordingly. The polarization information system provides a necessary solution for the accurate application of a specific algorithm, since it can ensure the accuracy of acquired image information. The key point of the proposed algorithm lies in accurately estimating the parameters in the polarization defogging model. Based on the normal threshold distribution comparison, it can avoid the highlight portion of the non-sky area in the image and accurately estimate the airlight intensity at infinite distance so as to effectively reduce the color distortion of the bright area. The median filtering algorithm is used to obtain the airlight degree of polarization by using three obtained polarization scenes. At last, this article analyzes the experimental results through defogging evaluation indexes and compared the result obtained by this algorithm with others.

Median Filtering for Removal of Maximum Impulse Noise from Images with a Decision Based Model

Filtering the unwanted parameters will guarantee the quality of images for further operations. Sensors and other devices used to capture the images may be subjected to situations where it might not function as programmed. The medium in which the images are transformed may also face the same difficulties. In such cases, salt and pepper noise should be removed before the images are taken into considerations. Otherwise, processes like edge detection, image segmentation and object recognition will be facing hurdles and may not reveal the desired output. This proposal intends to solve such an issue by using an algorithm for median filtering for using filters with medians on highly affected images. The previous techniques used for filtering out noise are Standard Median Filtering (SMF), Adaptive Median Filtering (AMF) and they have shown relatively lesser performance than the proposed approach. Implementation includes a FPGA set up and strives to remove impulse noise to a great extent consuming lesser computation time. The quality of output's visual and quantitative metrics has outperformed the discussed previous models.

Signal processing algorithm of ship navigation radar based on azimuth distance monitoring

The effect of ship navigation radar signal processing has a great impact on the overall performance of the radar system. In this paper, the signal processing algorithm is studied. Firstly, the principle of radar azimuth and distance monitoring is introduced, then the pulse accumulation algorithm and median filtering algorithm are analyzed, and finally a sea clutter suppression algorithm based on sensitivity time control (STC) and a rain and snow clutter suppression algorithm based on constant false alarm rate are designed to improve the target monitoring performance of radar. In the test of the algorithm, the radar signal processing algorithm designed in this study has good precision as monitoring error of the target's azimuth and distance is controlled within 1%; and it also has a better suppression effect of sea clutter and rain and snow clutter, which can suppress the clutter well, improve the target clarity, and ensure the safe navigation of the ship. The experiment proves the effectiveness of the proposed algorithm and provides some theoretical basis for the better processing of radar signals, which is beneficial to improve the environment perception ability of ships in harsh environments and promote the further development of the navigation industry.

Edge-area form removal of two-process surfaces with valley excluding method approach

This paper analyses the influence of edge-area form removal on surface texture parameters (from ISO 25178 standard). The study focused on assessing the effect of near-edge dimples on surface texture analysis. Areal form removal was performed by means of digital filtering methods (regular Gaussian filter, robust Gaussian regression filter, median filter and/or other algorithms). For minimisation of dimples distortion the valley digital excluding method (VDEM) with standard deviation determination procedure was applied to measure plateau-honed cylinder liner surfaces (more than 20 measured and/or 20 with digitally added dimples surfaces were studied). The measurements were performed with a stylus instrument, Talyscan 150, or a white light interferometer, Talysurf CCI Lite. It was assumed that application of VDEMwith further application of proposed digital filtering algorithms caused the decrease in dimples distortion with accordance to regular Gaussian regression and/or robust Gaussian regression filtering methods. It was also found that the near-edge dimples were more vulnerable to distortion than the centre-located oil pockets.

Priority Based Trimmed Median Filter for Removal of High Density Salt and Pepper Noise

This paper proposes an efficient and less complex priority-based trimmed median filter algorithm for restoring images corrupted by high density salt and pepper noise. Noisy pixel is replaced by trimmed median value of the horizontal and vertical adjacent four pixels, through this algorithm. If these four are 0's and 255's, then the next priority diagonal adjacent four pixels are used to calculate trimmed median for replacing noisy pixels. If these four are also found as 0's and 255's, then the noisy pixel is left unchanged until the next iteration. Experimental results on different greyscale and colour images show that the proposed algorithm outperforms the standard median filter, adaptive median filter, decision-based algorithm, modified progressive switching median filter and modified decision-based unsymmetric trimmed median filter.

Priority-based trimmed median filter for removal of high density salt and pepper noise

This paper proposes an efficient and less complex priority-based trimmed median filter algorithm for restoring images corrupted by high density salt and pepper noise. Noisy pixel is replaced by tri...

Ameliorated mean adaptive median filtering algorithm

Aiming at the problem that the quality of infrared image decreases due to the large amount of random noise in the process of collection and transmission of infrared image of electrical equipment, and the accuracy of automatic detection of electrical equipment decreases, based on the traditional adaptive median filter algorithm, the adaptive median filter is analyzed, which can filter only the salt and pepper noise below 25%. An improved mean adaptive median filtering algorithm is proposed to overcome the shortcomings of wave effect. Firstly, the filtering window is selected according to the decision setting condition, and then it is judged whether the K-mean value near the center point is a noise point, and if so, the window is increased, otherwise the average value is output. Finally, it is judged whether the value of the current pixel point is noise, and if so, the average value is output, otherwise, the current pixel value is output. The experimental results show that the algorithm can effectively filter salt and pepper noise and Gauss noise, while maintaining the image sharpness, and has good filtering performance on PSNR and MSE indicators.

Research on X-ray image processing technology for laser welded joints of aluminum alloy

Weldment X-ray image processing has great significance on the subsequent segmentation and extraction of weld seam and defects. In the current study, X-ray image for laser welding of aluminum alloy was adopted as experimental object. A new image denoising algorithm was proposed, which was combined with weighted adaptive median filter and noise-reduction algorithm based on wavelet transform. An improved adaptive fuzzy enhancement algorithm was put forward, which was based on traditional Pal-King fuzzy enhancement algorithm. Different noise-reduction algorithms were performed to denoise the X-ray image. Comprehensive evaluation of noise-reduction effect was conducted by comparing the processing effect, 3D grayscale distribution, and image quality evaluation index after different denoising algorithms. Moreover, fuzzy entropy and fuzzy index were used to estimate the enhancement effect of traditional Pal-King fuzzy enhancement algorithm and improved adaptive fuzzy enhancement algorithm. The results revealed that the noise-reduction effect and the image quality obtained by proposed algorithm were better than separately using weighted adaptive median filter or noise-reduction algorithm based on wavelet transform. Furthermore, after processing with improved adaptive fuzzy enhancement algorithm, the image detail information was more prominent, and sense of hierarchy was stronger.

Comparison of De-Noising Algorithms Technique

The concept of noise appears during the process of gathering the image into digital form: that is when the image is being created and it may also be introduced when the image is being transmitted. The presence of the noise usually degraded the quality of the image. De noising algorithms were employed in order to advance the value of the image. This paper tries to compare linear and non linear filtering algorithm. This study adopted image processing techniques to process 600 images dataset acquired from 60 different signers using vision based method. The acquired images were de-noised using Gaussian filter and Median filter algorithms. The outcomes of the two de-noising algorithms were compared using Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). The results of processed images for de-noising algorithms show that Median filter had higher PSNR of 47.7 than the Gaussian filter of 31.79, and lower MSE of 1.11 than Gaussian filter of 43.4.It was also ascertained that de-noised images with non-linear median filter had better quality than images de-noised by linear Gaussian filter.

Object Segmentation from Background of 2D Image

One of the difficult tasks in the image processing field and still not solved is segmentation of object from background of 2D image accurately. Therefore, a new method has been proposed for the purpose of segmenting the object from its background for the purpose of enhancing the images and obtains characteristics of the object without the rest of the region of the image. This process is important to provide optimal classification in the process of pattern recognition. Therefore, this paper proposed the method that includes several tasks, after loading the six files of images; this work applies the segmentation al- gorithm depending on the border and the color of the object. Finally, 2D median filtering algorithm was employed to remove noisy objects of various shapes and sizes. The algo- rithm was tested on variety images, and the results are high precision. In the other words, the proposed method is able to segment the objects from the background with promising results.

Research on mobile internet big data detecting method for the redundant data

To address the low efficiency of the traditional cleaning method, this paper presents a build path tree clean method based on split method for identification of the redundant data, through the traditional mobile internet big data cleaning process for identifying the redundant data is analysed, by using median filtering algorithm, the features of redundant data are extracted. Redundant data is classified by support vector machine (SVM), and the redundant data is identified by self-organising feature map. Based on this, the redundant data identification model is built, which can clean the redundant data in mobile internet big data. Comparing with the classical methods, the simulation results show that the proposed method has the advantages of high accuracy, good stability, high recall rate, short time consuming and low energy consumption.