Weighted Mean Filter Research Articles

An effective 2-stage method for removing impulse noise in images

In this paper, a robust 2-stage impulse noise removal system is proposed to remove impulse noise from extremely corrupted images. The contributions are in two-fold. First, a neuro-fuzzy based impulse noise detector (NFIDET) is introduced to identify the noisy pixels. NFIDET is a powerful noise detector that can handle image corruption even up to 90% with zero miss and false detection rate with a simple neuro-fuzzy structure. This is the best result among the other impulse noise detectors in the literature. Second, this paper presents a new approach for weight calculation of adaptive weighted mean filter by using robust statistical model. An adaptive robust weighted mean (ARWM) filter removes a detected noisy pixel by adaptively determining filtering window size and replacing a noisy pixel with the weighted mean of the noise-free pixels in its window. A Geman–McClure robust estimation function is used to estimate the weights of the pixels. Simulation results also show that the proposed robust filter substantially outperforms many other existing algorithms in terms of image restoration.

Adaptive weighted mean filtering algorithm based on city block distance

Adaptive weighted mean filtering algorithm based on city block distance

Computed tomography perfusion imaging denoising using Gaussian process regression

Brain perfusion weighted images acquired using dynamic contrast studies have an important clinical role in acute stroke diagnosis and treatment decisions. However, computed tomography (CT) images suffer from low contrast-to-noise ratios (CNR) as a consequence of the limitation of the exposure to radiation of the patient. As a consequence, the developments of methods for improving the CNR are valuable. The majority of existing approaches for denoising CT images are optimized for 3D (spatial) information, including spatial decimation (spatially weighted mean filters) and techniques based on wavelet and curvelet transforms. However, perfusion imaging data is 4D as it also contains temporal information. Our approach using Gaussian process regression (GPR), which takes advantage of the temporal information, to reduce the noise level. Over the entire image, GPR gains a 99% CNR improvement over the raw images and also improves the quality of haemodynamic maps allowing a better identification of edges and detailed information. At the level of individual voxel, GPR provides a stable baseline, helps us to identify key parameters from tissue time-concentration curves and reduces the oscillations in the curve. GPR is superior to the comparable techniques used in this study.

Application of Improved Median Filter on Image Processing

Median filter is the most common method of clearing image noise. This paper proposes improved algorithm of median filter to remove sale and pepper noise of image. According to the characteristics of salt and pepper noise, the algorithm detects image noise, and establishes noise marked matrix, without processing the pixels marked as signal. The signal of the pixel is marked as not treated, labeled according to their pixel noise pollution in the neighborhood to take a different pixel weighted mean filter window size, weight pixel region by the noise points to determine the local histogram. Matlab experiments show that improved median filter can greatly reduce the time of clears image noise and it performs better than median filters on noise reduction while retaining edges of an image.

에지정보를 고려한 복합잡음 제거를 위한 영상복원에 관한 연구

In image signal processing, image signal is corrupted by various noises and caused the degradation phenomenon. And Images often corrupted by AWGN(additive white gaussian noise) and impulse noise which called mixed noise. In this paper, the algorithm is proposed to remove mixed noise while keeping edge information. The proposed algorithm first classifies the noise type, if the classify result is AWGN, then the mean of the output after using self-adaptive weighted mean filter and median value will be the outfiltering value. And if the noise type is impulse noise, then the noise is removed by a modified nonlinear filter. Also we compare existing methods through the simulation and using PSNR(peak signal to noise ratio) as the standard of judgement of improvement effect. The result of computer simulation on test images indicates that the proposed method is superior to traditional filtering algorithms.

Numerically Filtering Based on PLC and the Application in the Control System of the Intelligent Building

This article introduces the way Arithmetic Mean Filtering, Weighted Mean Filtering and Inertia Filtering are applied in dealing with analog signals in PLC control system. Apart from presenting the design of the corresponding numerical filtering procedure, it provides a concrete illustration by citing a case where numerical filtering is applied in Intelligent Building Control System. Facts show Application of numerical filtering effectively strengthens the liability of the system.

Restoration of Images Contaminated by Mixed Gaussian and Impulse Noise using a Complex Method

Many approaches to image restoration are aimed at removing either gauss or impulse noise. This is because both types of degradation processes are distinct in nature, and hence they are easier to manage when considered separately. Nevertheless, it is possible to find them operating on the same image, which produces a hard damage. This happens when an image, already contaminated by Gaussian noise in the image acquisition procedure, undergoes impulsive corruption during its digital transmission. Here we proposed an algorithm first judge the type of the noise according to the difference values of pixel's neighborhood region and impulse noise's characteristic. Then removes the gauss noise by modified weighted mean filter and removes the impulse noise by modified nonlinear filter. The result of computer simulation on test images indicates that the proposed method is superior to traditional filtering algorithms. The proposed method can not only remove mixed noise effectively, but also preserve image details.

A Novel Spiking Cortical Model based Filter for Impulse Noise Removal

A novel spiking cortical model based switching mean filter for removing impulse noise is presented. In the proposed filter, the noise detector using spiking cortical model is first adopted to identify the pixels that are likely to be corrupted by impulse noise. Then the detected impulses are removed by the weighted mean filter while the noise-free pixels are left unaltered. Extensive simulations show that the proposed filter outperforms a number of existing decision-based filters due to its excellent performance in terms of effectiveness in image restoration. Because of its outstanding restoration performance, the proposed filter can be used for noise removal in numerous consumer electronics products such as digital camera and digital television.

Weighted mean filter based on local histogram

A weighted mean filtering algorithm was proposed for the filtering problem about gray-scale image that was polluted by sale and pepper noise of different degree. According to the characteristic of salt and pepper noise, the algorithm detected image noise, established noise marked matrix, without processing the pixels marked as signal. As for the pixels marked as noise, weighted mean filtering based on degree of pollution of its neighbor pixels with different window size was carried out. The weight of pixel was determined by the local histogram of noise point region. The result of image simulation shows that the algorithm can suppress noise efficiently and preserve the image detail information. Finally, the comparison of weighted mean filtering and other improved algorithms proves the effectiveness of the algorithm.

On-Machine Profile Measuring System for Grinding Surface of Aspheric Lens

In this paper, an on-machine profile measuring system for aspheric ground surface was presented, which involved data processing with improved two-dimensional weighted mean filtering and free form surface fitting. To verify the reliability of this system, a calibration experiment was done by comparing the measuring results of Ф320mm mirror between the WYKO 24 inch interferometer and the on-machine measuring system. The experiment results show that the on-machine measurement is effective and it can provide foundation for compensation machining. By comparing the measurement experiment results of the Keyence LC2430 with the WYKO interferometer, it is demonstrated that the deviation of PV is better than 0.23μm and the deviation of RMS better than 0.08μm.

Describe a new type of mean filter and its application in pressure distribution

A new center-weighted median and mean filter algorithm with self-adaptive time-space filter window was proposed.The algorithm combined weighted center median filter with weighted mean filter for denoising Gaussian noise and salt-pepper noise.The experimental results show that this algorithm can effectively remove the mixed noise in the pressure distribution data,while its computing time is less than that of other linear algorithms of wavelet,Wiener filter and so on.

Impulse Noise Removal Using Directional Difference Based Noise Detector and Adaptive Weighted Mean Filter

A two-stage algorithm, called switching-based adaptive weighted mean filter, is proposed to remove salt-and-pepper noise from the corrupted images. First, the directional difference based noise detector is used to identify the noisy pixels by comparing the minimum absolute value of four mean differences between the current pixel and its neighbors in four directional windows with a predefined threshold. Then, the adaptive weighted mean filter is adopted to remove the detected impulses by replacing each noisy pixel with the weighted mean of its noise-free neighbors in the filtering window. Numerous simulations demonstrate that the proposed filter outperforms many other existing algorithms in terms of effectiveness in noise detection, image restoration and computational efficiency.

영상에서 임펄스 잡음제거를 위한 적응력 있는 가중 평균 필터

본 논문은 영상을 획득할 때 잡음센서나 통신채널 불량으로 흔히 생기는 임펄스 잡음을 효율적으로 제거하는 방법에 대해 논의 하고자 한다. 제안된 방법은 잡음 픽셀 검출과 추정이라는 두 단계에 의해 이루어진다. 임펄스 잡음 검출기를 통하여 영상 전체에 걸쳐 잡음 픽셀여부를 검출한 후 잡음 픽셀로 판정되면 주변의 잡음 픽셀 개수에 따라 적응력 있게 <TEX>$3{\times}3$</TEX> 윈도우의 가중평균 혹은 <TEX>$5{\times}5$</TEX> 윈도우의 가중평균을 사용하여 추정한다. 제안된 방법의 성능을 평가하기 위해 영상실험을 통하여 기존의 잡음 제거 방법들과 정성적인 비교, PSNR과 MAE를 통한 정량적인 비교 그리고 수행 시간을 측정한 결과 제안된 방법은 잡음 제거는 물론 원영상에 대한 상세한 정보 보존력이 뛰어나고 수행 시간 면에서도 우수함을 보였다. In this work, a new adaptive weighted mean filter is proposed for preserving image details while effectively suppressing impulse noise. The proposed filter is based on a noise pixel detection-estimation strategy. All the pixels are first detected using an impulse noise detector. Then the detected noise pixels are replaced with the output of the weighted mean filter over adaptive working window according to the rate of corrupted neighborhood pixels, while noise-free pixels are left unaltered. We compare the proposed filter to other existing filters in the qualitative measure and quantitative measures such as PSNR and MAE as well as computation time to verify the capability of the proposed filter. Extensive simulations show that the proposed filter performs better than other filters in impulse noise suppression and detail preservation without increasing of running time.

An Efficient Procedure for Removing Random-Valued Impulse Noise in Images

In this letter, we present an efficient algorithm for the removal of random-valued impulse noise from a corrupted image by using a reference image. The proposed method uses a statistic of rank-ordered relative differences to identify pixels which are likely to be corrupted by impulse noise. Once a noisy pixel is identified, its value is restored by a simple weighted mean filter. Simulation results indicate that our algorithm provides a significant improvement over many other existing techniques.

Object extraction from an image of wear particles on a complex background

The object extraction of a debris image is an important basic task in identifying wear particles in ferrographic analysis. However, there is some difficulty in object extraction because of noise jamming in the original debris image. In the present study, two methods of image enhancement—weighted mean filtering and adaptive median filtering—were applied in order to improve the image quality. Then, the adaptive thresholding selection method was used, which is based on an improved debris image. Finally, the effective segmentation of the debris image and the automatic extraction of debris objects were realized. At the same time, targetting the characteristics of low proportion of an object in the total image, a novel method of adaptive thresholding selec- tion was put forward, which is based on the Ostu thresholding method. The segmentation results along with the debris image prove that the current method can give more precise and accurate segmentation of objects than the classical methods. The results also showed that methods in the present paper were concise and effective, which provides an important basis for the further study of debris recognition, fault diagnosis, and condition monitor- ing of machines. DOI: 10.1134/S1054661806040109

Image restoration techniques based on fuzzy neural networks

By establishing some suitable partitions of input and output spaces, a novel fuzzy neural network (FNN) which is called selection type FNN is developed. Such a system is a multilayer feedforward neural network, which can be a universal approximator with maximum norm. Based on a family of fuzzy inference rules that are of real senses, a simple and useful inference type FNN is constructed. As a result, the fusion of selection type FNN and inference type FNN results in a novel filter---FNN filter. It is simple in structure. And also it is convenient to design the learning algorithm for structural parameters. Further, FNN filter can efficiently suppress impulse noise superimposed on image and preserve fine image structure, simultaneously. Some examples are simulated to confirm the advantages of FNN filter over other filters, such as median filter and adaptive weighted fuzzy mean (AWFM) filter and so on, in suppression of noises and preservation of image structure.

Design of data‐dependent weighted mean filters using counter propagation networks

Design of data‐dependent weighted mean filters using counter propagation networks

Design of data‐dependent weighted mean filters using counter propagation networks

Design of data‐dependent weighted mean filters using counter propagation networks

Design of data-dependent weighted mean filters using counter propagation networks

Design of data-dependent weighted mean filters using counter propagation networks

Some Modifications of Gradient Weighted Filters

Gradient weighted filters are locally adaptive weighted mean filters. In this paper, a general formulation of gradient weighted filters with some characteristic parameters was derived first from existing gradient weighted filters. Then we propose some modifications by varying these parameters. We modify gradient inverse weighted filters, characterize filters into first and second order filters, and propose Π filters. Moreover, an imposed criterion for second order filters to preserve fine details was introduced to promote the existing gradient weighted filters. Finally, a criterion to combine first and second order filters was proposed to remove noise with mixed types. Throughout this paper, rational analysis and experimental results demonstrate the efficiency of the proposed methods.