Median Filtering Algorithm Research Articles

Edge-Preserving Median Filter and Weighted Coding with Sparse Nonlocal Regularization for Low-Dose CT Image Denoising Algorithm

The impulse noise in CT image was removed based on edge-preserving median filter algorithm. The sparse nonlocal regularization algorithm weighted coding was used to remove the impulse noise and Gaussian noise in the mixed noise, and the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) were calculated to evaluate the quality of the denoised CT image. It was found that in nine different proportions of Gaussian noise and salt-and-pepper noise in Shepp-Logan image and CT image processing, the PSNR and SSIM values of the proposed denoising algorithm based on edge-preserving median filter (EP median filter) and weighted encoding with sparse nonlocal regularization (WESNR) were significantly higher than those of using EP median filter and WESNR alone. It was shown that the weighted coding algorithm based on edge-preserving median filtering and sparse nonlocal regularization had potential application value in low-dose CT image denoising.

Research on QR image code recognition system based on artificial intelligence algorithm

Abstract The QR code recognition often faces the challenges of uneven background fluctuations, inadequate illuminations, and distortions due to the improper image acquisition method. This makes the identification of QR codes difficult, and therefore, to deal with this problem, artificial intelligence-based systems came into existence. To improve the recognition rate of QR image codes, this article adopts an improved adaptive median filter algorithm and a QR code distortion correction method based on backpropagation (BP) neural networks. This combination of artificial intelligence algorithms is capable of fitting the distorted QR image into the geometric deformation pattern, and QR code recognition is accomplished. The two-dimensional code distortion is addressed in this study, which was a serious research issue in the existing software systems. The research outcomes obtained after emphasizing on the preprocessing stage of the image revealed that a significant improvement of 14% is observed for the reading rate of QR image code, after processing by the system algorithm in this article. The artificial intelligence algorithm adopted has a certain effect in improving the recognition rate of the two-dimensional code image.

Image Noise Removal Method Based on Improved Nonlocal Mean Algorithm

Aiming at the problem of unclear images acquired in interactive systems, an improved image processing algorithm for nonlocal mean denoising is proposed. This algorithm combines the adaptive median filter algorithm with the traditional nonlocal mean algorithm, first adjusts the image window adaptively, selects the corresponding pixel weight, and then denoises the image, which can have a good filtering effect on the mixed noise. The experimental results show that, compared with the traditional nonlocal mean algorithm, the algorithm proposed in this paper has better results in the visual quality and peak signal-to-noise ratio (PSNR) of complex noise images.

Human motion analysis and action scoring technology for sports training based on computer vision features

With the advent of the information age, computer-related application research has become more and more extensive, human motion analysis and action scoring based on computer vision have gradually become the focus of attention. In order to adapt to the development of the times and solve the problems related to the analysis of human motion, the experiment analyzed the similarity of eight common human movement behaviors, analyze the movement speed of men and women under sports training, and analyzed the accuracy of the human body motion recognition model in the two cases of the original gray data and the frame difference channel, finally, the denoising performance of four different algorithms of SMF, EMF, RAMF and median filter algorithm in digital image processing is analyzed. The final result shows that there is a big similarity between the same kind of human movement behavior, the accuracy rate of the frame difference channel human body recognition model is higher than that of the original gray data recognition model, and digital image processing median filter algorithm has good image denoising performance.

Denoising Medical Images Using Machine Learning, Deep Learning Approaches: A Survey.

Several denoising methods for medical images have been applied, such as Wavelet Transform, CNN, linear and Non-linear methods. In this paper, A median filter algorithm will be modified and the image denoising method to wavelet transform and Non-local means (NLM), deep convolutional neural network (Dn- CNN), Gaussian noise, and Salt and pepper noise used in the medical image is explained. PSNR values of the CNN method are higher and showed better results than different filters (Adaptive Wiener filter, Median filter, and Adaptive Median filter, Wiener filter). Denoising methods performance with indices SSIM, PSNR, and MSE have been tested, and the results of simulation image denoising are also presented in this article.

New Positions of Triton Based on Gaia DR2 and New Image-processing Methods

Abstract The large time span and precise observational data of natural satellites is of great significance for updating their ephemerides and studying their dynamic characteristics. With the help of the new image-processing methods and the Gaia DR2 catalog, all CCD images of Triton taken with the 1.56 m telescope of Shanghai Astronomical Observatory during 2005–2009 were reanalyzed. The median filtering algorithm is used for image preprocessing to remove the influence of the halo of Neptune, and an upgraded modified moment, called the intensity-square-weighted centroiding method, is applied to determine the centroids of the stars and Triton. A total of 2299 positions of Triton were obtained, including 263 new observed positions and 2036 updated observed positions. Such five-year time span data with high precision will be very helpful to improve the orbit parameters of Triton.

Wearable Low-power Bio-signals Wireless Sensing Node Design

This paper realizes the design of wearable bio-signals multi-sensing nodes through ADS1292 series ECG acquisition chip, LMT70 IoT temperature sensing chip, MPU6050 spatial motion sensing chip and HC-06 Bluetooth transmission chip, which includes real-time monitoring of three-lead acquisition of biological ECG signals, real-time display of heart rate changes and ECG, real-time monitoring of biological body surface temperature and motion state, wireless communication of collected data. The median filtering algorithm and mathematical morphology are used to eliminate the baseline drift and the 50Hz trap to eliminate the power frequency signal interference. The design has the advantages of high common mode rejection ratio, high signal-to-noise ratio, low noise, low power consumption and low cost, which meets the needs of modern portable networked monitoring devices.

Conveyorized implementation of ASWM image filter on PLD

The object of research is the adaptive switching weighted median image filter (ASWM) algorithm. This algorithm is one of the most effective in the field of impulse noise suppression. The computational complexity and algorithmic features of this adaptive nonlinear filter make it impossible to implement a filter that works in real time on modern PLD microcircuits. The most problematic areas of the algorithm are the weight coefficient estimation cycle, which has no limit on the number of iterations and contains a large number of division operations. This does not allow implementing the filter on PLDs with a sufficiently effective method. In the course of the research, the programming model of the filter in Python was used. The performance of the algorithm was assessed using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics. Modeling made it possible to find out empirically the number of iterations of the cycle for estimating the weight coefficients at different levels of noise density and to estimate the effect of artificial limitation of the maximum number of iterations on the filter performance. Regardless of the intensity of the noise impact, the algorithm performs less than 40 iterations of the evaluation cycle. Let’s also simulate the operation of the algorithm with different variants of the division module implementation. The paper considers the main of them and offers the most optimal in terms of the ratio of accuracy/hardware costs for implementation. Thus, a modified algorithm was proposed that does not have these disadvantages. Thanks to modifications of the algorithm, it is possible to implement a pipelined ASWM image filter on modern PLDs. The filter is synthesized for the main families of Intel PLDs. The implementation, which is not inferior in terms of SSIM and PSNR metrics to the original algorithm, requires less than 65,000 FPGA logical cells and allows filtering of monochrome images with FullHD resolution at 48 frames/s at a clock frequency of 100 MHz.

Enhanced Fingerprinting Based Indoor Positioning Using Machine Learning

Due to the inability of the Global Positioning System (GPS) signals to penetrate through surfaces like roofs, walls, and other objects in indoor environments, numerous alternative methods for user positioning have been presented. Amongst those, the Wi-Fi fingerprinting method has gained considerable interest in Indoor Positioning Systems (IPS) as the need for line-of-sight measurements is minimal, and it achieves better efficiency in even complex indoor environments. Offline and online are the two phases of the fingerprinting method. Many researchers have highlighted the problems in the offline phase as it deals with huge datasets and validation of Fingerprints without pre-processing of data becomes a concern. Machine learning is used for the model training in the offline phase while the locations are estimated in the online phase. Many researchers have considered the concerns in the offline phase as it deals with huge datasets and validation of Fingerprints becomes an issue. Machine learning algorithms are a natural solution for winnowing through large datasets and determining the significant fragments of information for localization, creating precise models to predict an indoor location. Large training sets are a key for obtaining better results in machine learning problems. Therefore, an existing WLAN fingerprinting-based multi-story building location database has been used with 21049 samples including 19938 training and 1111 testing samples. The proposed model consists of mean and median filtering as pre-processing techniques applied to the database for enhancing the accuracy by mitigating the impact of environmental dispersion and investigated machine learning algorithms (kNN, WkNN, FSkNN, and SVM) for estimating the location. The proposed SVM with median filtering algorithm gives a reduced mean positioning error of 0.7959 m and an improved efficiency of 92.84% as compared to all variants of the proposed method for 108703 m2 area.

Image Denoising Based on Adaptive Sector Rotation Median Filter

By analyzing the characteristics of different median filtering algorithms, an adaptive fan rotation median filtering method based on the standard median filtering method is proposed to restore the details of the image to the maximum extent for achieving better pepper and salt noise removal effect. The proposed method can change the window size adaptively according to the pollution degree in the window, and calculate the gray difference value for different areas, judge the correlation between the center point and different areas according to the gray difference value, and take the median value of the highest correlation area as output, which can restore the details of the image while removing the noise. The experimental results show that the new algorithm has a high similarity with the original image structure and a high peak signal-to-noise ratio after filtering, and can better recover the image details. Compared with other methods, the noise removal effect and detail recovery effect are better.

Automatic Recognition and Correction of Volleyball Players’ Release Angle Based on Feature Statistics

The constant reform of the competition rules has promoted the innovation of volleyball techniques and tactics. In order to improve the training efficiency and competitive level of volleyball players, this study designed a volleyball player shooting angle automatic recognition and correction method based on the process of feature statistics. Firstly, the basic structure of the information acquisition system is analyzed, and the acquisition process is determined. Then, grayscale and binarization operations are carried out for color‐moving images to separate their foreground and background, and a median filtering algorithm is used to remove the image noise. Then, the image pyramid of different sizes is generated by the filter. Based on setting the datum direction, the feature of volleyball shooting is extracted by using the line formula. On this basis, we construct a support vector machine (SVM) classifier to statistically classify the features, use the histogram additive kernel support vector machine method to obtain the lens angle recognition results, and correct the lens angle through feature point matching. Simulation experiments show that this method can effectively remove image noise and make the image signal‐to‐noise ratio higher, and it can effectively identify whether volleyball players’ release Angle is correct, to achieve the purpose of timely correction.

Multi‐source information art painting fusion interactive 3D dynamic scene virtual reality technology application research

SummaryArt painting is a very important major in China's education system. Under the background of the new era, the country vigorously promotes the modernization of education and the development of intelligence. Many subjects of teaching have been more mature and carried out intelligent teaching based on virtual reality technology. But throughout the art painting profession, the research on this aspect is almost blank. The main reason is that there are deep academic barriers between art painting and computer science. Therefore, this paper puts forward the application research of multi‐source information art painting fusion interactive 3D dynamic scene virtual reality technology. This paper deeply analyzes the needs of art painting and its teaching reform and believes that the existing virtual reality technology cannot adapt to the teaching environment of art painting. In view of the possible inaccurate calculation and other problems, this paper puts forward the optimization scheme. The core of the scheme is to use the information integration advantages of multi‐source information fusion to provide more comprehensive and complete object information for 3D modeling of virtual reality technology, so as to improve the imaging quality of virtual reality technology. In order to meet the requirement of high precision, the calculation method is optimized, the calculation steps are simplified, and the sensor selection is optimized again. This paper introduces the design principle and operation steps of the optimization method in detail. In order to further verify the actual effect of this method, comparative analysis method is used to test the comprehensive performance. In order to ensure the test quality, the median filtering algorithm and fuzzy algorithm are compared. Through the analysis and comparison of the data, the optimization method in this paper greatly improves the accuracy of calculation and the comprehensive performance is improved.

Improved probabilistic decision‐based trimmed median filter for detection and removal of high‐density impulsive noise

This study focuses on the detection and expulsion of noisy pixels from an image contaminated by impulsive noise. A noise detection approach is developed to avoid the misinterpretation of noise-free pixel as noisy. In order to design the noise removal algorithm, a probabilistic decision-based improved trimmed median filter (PDITMF) algorithm is proposed which is intended to work out the conflict related to the even number of noise-free pixels in the trimmed median filter. It deploys two new estimation techniques for de-noising, namely, improved trimmed median filter (ITMF) and patch else ITMF (PEITMF) as per noise density. At last, the noise detection approach is applied in the proposed PDITMF to build up a new technique called a probabilistic decision-based adaptive improved trimmed median filter (PDAITMF) algorithm. The proposed algorithms, PDITMF and PDAITMF experiment with many standard sample images. Simulation results show the proposed algorithms are capable of detecting and de-noising the contaminated image very efficiently and have a better visual representation. Under the authors' knowledge, the PDAITMF outperforms recently reported algorithms in context to peak signal-to-noise ratio as well as an image enhancement factor with the lower execution time at all noise densities.

Computer-Integrated Device for Acidity Measurement Monitoring in Greenhouse Conditions with Compensation of Destabilizing Factors

The purpose of the article is to improve procedures of computerized monitoring and control of technological processes of growing greenhouse crops by substantiating methods of improving the accuracy of computer-integrated devices for measuring irrigation solution acidity. The article solves the topical scientific and applied problem of determining the conversion characteristics of computerized acidity monitoring systems with integral and differential assessment of their metrological parameters. Theoretical and experimental studies were obtained based on structural-algorithmic synthesis methods for information-measuring systems; methods of mathematical planning of experiments; regression analysis of experimental data and the concept of uncertainty. The computerized acidity meter was implemented on the basis of an ion-selective pH electrode, Arduino microprocessor platform, and ThingSpeak cloud computing service. The relative total boundary uncertainty of acidity measurement is not more than ±1.1 %. Methods of compensating of the random component of uncertainty based on the median filtering algorithm and additional uncertainty from the destabilizing effect of temperature were introduced when implementing the measuring device. Promising areas of priority research to improve the efficiency of the developed computerized acidity meter were justified. The developed device can be used in the complex automation of greenhouse cultivation processes. The developed and implemented measuring tool can be used when planning agricultural operations in greenhouse conditions.

Study on 3D Pore Label of Porous Materials Based on CT Images

Microscopic pore structure is one of the important factors affecting macro porous materials physical properties, pore structure research for characterization of physical properties of porous materials is of great significance. In order to study the three-dimensional parameters of porous materials in each pore structure, such as pore size, volume, specific surface area, fractal and connectivity, it is necessary to label each pore structure in the three-dimensional structure. Two-dimensional CT image sequences in porous material as the research object, three-dimensional target marking algorithm is proposed. The sequence images using median filter algorithm to remove noise, through the automatic threshold segmentation algorithm, get the binary image. The boundary information of each pore is computed by Using the algorithm of two-dimensional multi-target label in each layer image. The 3D pore structure is labelled by the calculation of the projection relationship between the targets in the tomography image. The labelled result of pore structure is verified by single and whole reconstruction with marching cube algorithm. Results show that the porous three-dimensional pore structure was marked accurately, which lays a foundation for the calculation of 3D pore structure parameters.

Application of Different Median Filter Algorithms for Welding Defects Clarification in Radiographic Images

The main aim of the inspections is to ensure that the quality of the welds meets the design requirements and operating conditions, as well as meeting safety and reliability requirements in many industrial sectors. There are many different ways of non-destructive testing NDT are employed for checking welds. The radiographic testing RT is one of the most prevalent and widely used processes in the industry to detect both surface and subsurface defects. In recent years, industrial radiography has been well developed to be used alongside with the combination of techniques of image processing to enhance both the process of inspection and the time of operation. The RT images are often affected by some external surroundings and degraded by noises during the process of acquisition of them. The filtering process is intended to recover the original image from the corrupted one. Median filtering can be considered as an appropriate procedure since it is used by many researchers. In this paper, a comparison among four types of median filter is used to get the best one, to adopting it in welding inspection images. The peak signal to noise ratio (PSNR) and root means square error (RMSE) indexes are used to perform the quality of image filtering techniques. The results shown that the median filter is a good tool for clarification of welding defects and both adaptive median filter (AMF) and decision based median filter (DBMF) gives the best results respectively. The results obtained were consistent with the results of the engineering examination for qualified and certified welding defects inspectors

A denoising strategy for the improvements of PPGi’s signal-to-noise-ratio

Photoplethysmographic imaging (PPGi) is a burgeoning technology to monitor physiological parameters. Under the background of information technologies’ development, both the PPGi signal’s collecting set-ups and its data processing methods have been widely studied. However, the unsatisfactory signal-to-noise-ratio is still an obstacle to its productization. For this problem, an appropriate denoising strategy would be a feasible solution. Therefore, through the analysis and screening of Butterworth Low Pass filter, Butterworth High Pass filter, Median filter, Wavelet Transform, Hilbert-Huang Transform and Independent Component Correlation Algorithm, three of them are combined to form 3 tactics, their effects in improving signal-to-noise-ratio of PPGi signals are evaluated. It is found, the integration of Butterworth Low Pass filter, Median filter and Wavelet Transform (BMW) can acquire the best signal-to-noise-ratio (31.42dB) in the designed strategies. Furthermore, these denoised PPGi signals by the three tactics are used to calculate the blood pressure (BP) and heart rate (HR). The comparisons with the actually BP and HR data which are measured by a commercial sphygmomanometer also indicate that the close-to-actual BP and HR are obtained from the BMW denoised PPGi signals.

Parallelization implementation of the multi‐scale retinex image‐enhancement algorithm based on a many integrated core platform

SummaryImage‐enhancement algorithms, for example, median filtering algorithms, Gaussian filtering algorithms, and the multiscale Retinex (MSR) algorithm, are widely used in unmanned aerial vehicle (UAV) image processing to resolve the problems of poor clarity, insufficient contrast, and weak adaptability of the aerial images. Aiming to improve the low efficiency of processing a large volume of UAV images using the MSR algorithm, this research realized a parallel MSR algorithm using the OpenMP programming model based on Intel's many integrated core (MIC) platform. First, the principle and serial implementation of the MSR algorithm were reviewed in detail, and the algorithm's hotspots were determined with the Intel VTune tool. Then, the corresponding parallel algorithm was designed and implemented. After checking the correctness of the parallel algorithm, systematical experiments on UAV images of different sizes were carried out. According to the experiments performed in the course of this work, the parallel MSR interpolation algorithm attained a speedup factor of 32 on two Intel MIC acceleration cards with 60 cores, which indicated that the parallel algorithm greatly reduced processing time and maximized speed and performance.

High-throughput three-dimensional visualization of root system architecture of rice using X-ray computed tomography

BackgroundX-ray computed tomography (CT) allows us to visualize root system architecture (RSA) beneath the soil, non-destructively and in a three-dimensional (3-D) form. However, CT scanning, reconstruction processes, and root isolation from X-ray CT volumes, take considerable time. For genetic analyses, such as quantitative trait locus mapping, which require a large population size, a high-throughput RSA visualization method is required.ResultsWe have developed a high-throughput process flow for the 3-D visualization of rice (Oryza sativa) RSA (consisting of radicle and crown roots), using X-ray CT. The process flow includes use of a uniform particle size, calcined clay to reduce the possibility of visualizing non-root segments, use of a higher tube voltage and current in the X-ray CT scanning to increase root-to-soil contrast, and use of a 3-D median filter and edge detection algorithm to isolate root segments. Using high-performance computing technology, this analysis flow requires only 10 min (33 s, if a rough image is acceptable) for CT scanning and reconstruction, and 2 min for image processing, to visualize rice RSA. This reduced time allowed us to conduct the genetic analysis associated with 3-D RSA phenotyping. In 2-week-old seedlings, 85% and 100% of radicle and crown roots were detected, when 16 cm and 20 cm diameter pots were used, respectively. The X-ray dose per scan was estimated at < 0.09 Gy, which did not impede rice growth. Using the developed process flow, we were able to follow daily RSA development, i.e., 4-D RSA development, of an upland rice variety, over 3 weeks.ConclusionsWe developed a high-throughput process flow for 3-D rice RSA visualization by X-ray CT. The X-ray dose assay on plant growth has shown that this methodology could be applicable for 4-D RSA phenotyping. We named the RSA visualization method ‘RSAvis3D’ and are confident that it represents a potentially efficient application for 3-D RSA phenotyping of various plant species.

Property‐based shadow detection and removal method for licence plate image

Shadow detection and removal is a classic research in the field of image processing. This work aims to address the problem of shadow detection and removal for licence plate. The shadow detection and removal method based on inherent properties of licence plate is presented in this study. First, an S–V-channel-property-based shadow detection algorithm is investigated to extract the background-shaded area of licence plate. Afterwards, a shadow edge location algorithm is proposed to detect the complete shadow edge. Then, the shadow is removed on H, S, and V channels with different strategies, respectively. Also, a filter-based median filtering algorithm is employed to remove the pseudo-edge. Finally, the proposed method is verified through comparing with other generic shadow detection and removal methods quantitatively and qualitatively.