Adaptive Median Filtering Algorithm Research Articles

ECG R-Wave Detection and Its Application in Left Ventricular Assist Device

Heart failure is one of the major diseases endangering human life. As a transitional support treatment before heart transplantation, the left ventricular assist device (LVAD) has significantly improved the quality of life and survival rate of patients with end-stage heart failure. In the automatic detection of electrocardiogram (ECG), the detection of QRS wave groups is the most critical aspect, which affects the correctness and accuracy of subsequent data analysis and processing. The paper used the data from the MIT-BIH database and the data collected from human ECG as the original number of samples for further processing. It processed the data through a high-order finite impulse response (FIR) low-pass filter and Shannon energy algorithm, and added the use of a high-order adaptive median filter algorithm on a field programmable gate array (FPGA) to minimize the noise of the processed real-time data. Finally, the tested ECG R-wave was used to drive the LVAD. After successful simulation by MATLAB and Modelsim software, the scheme of the real-time ECG signal controlling blood pump system was realized on the FPGA platform. The experiment showed that the method proposed could accurately extract the R-wave and control the LVAD to pump blood simultaneously.

Intelligent Recognition Technology for the Segmentation of Traffic Indication Images Concerning Different Pavement Materials.

Traffic indication is an important part of the road environment, providing information about road conditions, restrictions, prohibitions, warnings, and the current status related to the flow of the traffic and other navigational aspects. The shape, color, and pictogram of a traffic indication are encoded into the visual characteristics of traffic signs. Not paying attention to these traffic signs could lead directly or indirectly to traffic accidents. In this article, the support traffic indication vector recognition (STIVR) method is proposed to classify the best signal detection to avoid traffic congestion and accidents. The proposed STIVR recognizes the traffic indication system automatically, reduces occurrences of traffic accidents, and helps drivers move safely on different pavement materials. Besides, the adaptive median filter (AMF) algorithm is used to pre-process and protect the traffic indication images without obscuring them. Thus, it indicates the edge of the non-smoothed nasty ferment from the service. In the detection of traffic events, indication images are enhanced, pre-treated, and divided according to symbols and their characteristics such as color, shape, or both. The output becomes a segmented image, including the available space identified as a road sign. The experimental results show that the proposed method functions well; achieves a sufficiently higher process speed and better segmentation of traffic indications and more accuracy in recognition of the objects. For example, the proposed method reaches a higher sensitivity performance of 96%.

Multi-Model Detection of Lung Cancer Using Unsupervised Diffusion Classification Algorithm

Lung cancer is a curable disease if detected early, and its mortality rate decreases with forwarding treatment measures. At first, an easy and accurate way to detect is by using image processing techniques on the cancer-affected images captured from the patients. This paper proposes a novel lung cancer detection method. Firstly, an adaptive median filter algorithm (AMF) is applied to preprocess those images for improving the quality of the affected area. Then, a supervised image edge detection algorithm (SIED) is presented to segment those images. Then, feature extraction is employed to extract the mean, standard deviation, energy, contrast, etc., of the cancer-affected area. Finally, an unsupervised diffusion classification (UDC) algorithm is explored to narrow down the affected areas. The proposed lung cancer detection method is implemented on two datasets obtained from standard hospital real-time values. The experiment results achieved superior performance in the detection of lung cancer, which demonstrates that our new model can contribute to the early detection of lung cancer.

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.

An Improved WNNM Algorithm for Image Denoising

The traditional weighted nuclear norm minimization (WNNM) has excellent performance for the removal of non-sparse noise such as Gaussian noise, but attains bad performance for the removing of salt&pepper noise and mixed noise of Gaussian noise and salt&pepper noise. This paper proposes an improved WNNM image denoising algorithm. WNNM can effectively remove non-sparse noise such as Gaussian noise and adaptive median filtering algorithm can effectively remove sparse noise such as salt and pepper noise; the improved algorithm combines the characteristics of WNNM and adaptive median filtering. The experimental data demonstrate that the improved WNNM algorithm has better denoising effect than WNNM algorithm.

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.

Improved Adaptive Median Filter Algorithm for Removing Impulse Noise from Grayscale Images

Digital image is often degraded by many kinds of noise during the process of acquisition and transmission. To make subsequent processing more convenient, it is necessary to decrease the effect of noise. There are many kinds of noises in image, which mainly include salt and pepper noise and Gaussian noise. This paper focuses on median filters to remove the salt and pepper noise. After summarizing the main disadvantages of the conventional median filters, this paper proposes a new kind of median filter algorithm based on the detection of impulse noise points. The performance of the proposed algorithm is compared with the conventional standard median filter (SMF), extremum median filter (EMF), and adaptive median filter (AMF). Experimental results under various noise intensities show that the proposed method has better denoising performance and detail preservation compared with the other denoising methods.

Oil spill detection method using X-band marine radar imagery

A viable method to implement oil spill detection and monitoring based on marine radar is proposed. The primary data of this study are obtained from the X-band marine radar of the teaching–training ship, YUKUN, of the Dalian Maritime University on July 21, 2010, when a pipeline burst and an oil spill accident occurred at the Xingang Port in Dalian. Aiming at the working characteristics of marine radar, the adaptive median filter algorithm is improved to eliminate the radar shared-frequency interference by adding the identification of noise points and resetting the neighborhood window. A power attenuation correction method is proposed to solve the uneven distribution in resolution and echo intensity by acquiring the average power distribution of radar images simultaneously. Oil spill will be easily detected from different sea backgrounds after morphological processing, gray segmentation, and image smoothing. Comparison with the images extracted from a thermal infrared sensor on the same monitoring point demonstrates the validity of the extraction method for oil spill based on X-band marine radar.

Dynamic Window-based Adaptive Median Filter Alogorithm

In response to the flaw that the median filtering algorithm has a poor handling capacity to high-density and fine texture noise, a Dynamic window-based adaptive median filter algorithm is proposed. According to the associated level between noise-point information and the surrounding, the new algorithm adjust, the Noise point filter value, which can get a better deal with the details of the images. The adaptive strategies strengthen the performance of the filtering de-noising algorithm, which is good at dealing the high-density noise. Through simulation analysis, the new algorithm is better than other algorithms.

Application of Improved Median Filtering Algorithm to Image De-Noising

Based on the shortcomings of standard median filtering and combined with the mean filtering, this paper puts forward two improved median filtering algorithms referred as the weighted fast median filtering algorithm and the weighted adaptive median filtering algorithm. The experiment results with MATLAB show that weighted fast median filtering algorithm has a significant effect on low-density impulse noise, and accelerates the speed of real-time processing. The weighted median filter could effectively eliminate the high-density impulse noise from polluted images, and better maintains the details of the original image.

Research on palmprint identification method based on quantum algorithms.

Quantum image recognition is a technology by using quantum algorithm to process the image information. It can obtain better effect than classical algorithm. In this paper, four different quantum algorithms are used in the three stages of palmprint recognition. First, quantum adaptive median filtering algorithm is presented in palmprint filtering processing. Quantum filtering algorithm can get a better filtering result than classical algorithm through the comparison. Next, quantum Fourier transform (QFT) is used to extract pattern features by only one operation due to quantum parallelism. The proposed algorithm exhibits an exponential speed-up compared with discrete Fourier transform in the feature extraction. Finally, quantum set operations and Grover algorithm are used in palmprint matching. According to the experimental results, quantum algorithm only needs to apply square of N operations to find out the target palmprint, but the traditional method needs N times of calculation. At the same time, the matching accuracy of quantum algorithm is almost 100%.

Adaptive Switching Median Filter Based on GA-BP Neural Network

For the poor performance of conventional filtering algorithms in removing salt and pepper noise from digital images under high noise density,an adaptive switching median filter algorithm based on BP neural network optimized by genetic algorithm (GA) is proposed to detect and remove salt and pepper noise from images. Firstly,the initial weights and thresholds of BP neural network are optimized by genetic algorithm.Then image pixels are devided into either signal or noise points by the trained network automatically. The detected noise points will be removed by adaptive switching median filter algorithm,but nothing to do with the signal points. Experiment results show that the proposed algorithm significantly outperforms the others and efficiently removes salt and pepper noise from digital images without distorting image details and textures .

임펄스 잡음 제거를 위한 개선된 적응 메디안 필터

영상전송 등의 과정에서 임펄스 잡음에 의한 영상의 열화가 발생하며, 이러한 잡음을 제거하기 위해 활발한 연구가 진행되고 있다. 현재 대표적인 임펄스 잡음제거 방법에는 SM 필터(Standard Median Filter)가 있지만, SM 필터는 임펄스 잡음 밀도가 커짐에 따라 에지 영역에 오류를 나타낸다. 따라서 본 논문에서는 영상의 에지를 보존하기 위해 또한 영상의 왜곡을 줄이기 위해 변형된 적응 메디안 필터 알고리즘을 제안하였으며, 시뮬레이션 결과 제안한 알고리즘은 여러 영역에서 모두 우수한 결과를 나타내었고, 평가기준으로 PSNR을 사용하였다. Image degradation caused by the impulse noise is generated in the process of image transmission and so on. It has been studied by many researchers in order to remove these noise. The representative impulse noise removal method includes SM filter. Though SM filter will indicate errors by the increasing of impulse noise density. Therefore, in this paper, in order to preserve the edges of the image, and reduce the distortion of the image, an improved adaptive median filter algorithm is proposed. In the simulation results, the algorithm showed excellent results in all several areas, and the PSNR is used as the criterion of evaluation.

Adaptive median filtering algorithm based on slope

For estimating and removing the salt-and-pepper noise point accurately in image,a new adaptive median filtering algorithm was proposed.Firstly,if the pixel in the center of n×n(n is an odd integer not less than three) template was the extreme value of all the pixels in the window,it was supposed to be probably a noise point.The pixel gray value in the sequence difference between the two scripts and a template sequence of the slope of the pixel gray value within the region were used to determine the mean quasi-adaptive noise point to be the real noise points.Finally,mean filtering was done on the noised pixels.Compared with median filter,the condition of detecting noises with this method has been largely enhanced.And the method can both effectively restrain noises and maintain details.

Filter Window Characteristics Based Fast Adaptive Median Filter

This paper describes a Filter Window Characteristics based fast adaptive median filter algorithm. Experimental results from the point of view, this adaptive median filter algorithm to determine the degree of pollution on the image to adjust adaptive filtering process, while maintaining the average speed median filter algorithm based on filtering noise effectively, and better protection of the image the edge of the details.

A Novel Image Denoising Method Based on Adaptive Median Filter Algorithm

Image denoising is a very important part in image pre-processing. Due to the problem, which the edge details in the denoised image easily lost with the increase of the template-window size, the image blurring is increased. This paper presents comprehensive analysis on the advantages and disadvantages of existing algorithms and proposes a new algorithm which is called as adaptive median filter algorithm. This algorithm combines the selected mask filter algorithm and median filtering algorithm together. Experimental results show that the algorithm can speed up and the image blurring can be reduced. It also gets a satisfactory effect on the high-density noise image.

Dynamic window-based adaptive median filter algorithm

Aiming to solve the problem that median filtering algorithm has poor processing capacity on high-density noise image and delicate texture image,a dynamic window-based adaptive median filter algorithm was proposed.According to the associated level between noise-point information and its surrounding,the new algorithm adjusted the noise-point filter value,which can deal with the details of the images better.The adaptive strategies of the new algorithm strengthen the denoising performance of the filtering algorithm,which is good at dealing with any density noise.The simulation analysis shows that the new algorithm can effectively improve the peak signal to noise ratio of the image,and the denoising effect is more satisfactory than other methods.

An adaptive median filter algorithm based on B-spline function

According to the B-spline convolution mask, first, the contrast sensitiveness (CS) is computed and then is viewed as a noise sensitiveness coefficient (NSC) to adaptively determine a noise-recogniz...