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Journal articles on the topic 'AMF (adaptive median filter)'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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Fachrunnisa, Nisa, Ari Usman, and Mufida Khairani. "Implementasi Noise Removal Dan Image Enhancement Pada Citra Digital Menggunakan Metode Adaptive Median Filter." Jurnal Ilmu Komputer dan Sistem Informasi 3, no. 1 (2024): 11–20. http://dx.doi.org/10.70340/jirsi.v3i1.95.

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Citra sebagai salah satu komponen multimedia memegang satu peranan sangat penting sebagai bentuk informasi visual. Citra memiliki karakteristik yang tidak dimiliki oleh teks, yaitu citra kaya dengan informasi. Meskipun sebuah citra kaya akan informasi, namun seringkali citra yang kita miliki mengalami penurunan mutu (degradasi) citra yaitu penurunan kualitas citra, misalnya karena mengandung cacat atau derau (noise), Metode Adaptive Median Filter (AMF) merupakan metode yang dirancang untuk menghilangkan masalah yang dihadapi dengan standar median filter. Adaptive median filter memiliki tujuan ganda yaitu menghapus impuls noise pada gambar dan mengurangi distorsi pada gambar. Filter ini juga memperhalus noise. Oleh karena itu penulis tertarik untuk melakukan “Implementasi Noise Removal Dan Image Enhancement Pada Citra Digital Dengan Menggunakan Metode Adaptive Median Filter”. Hal ini dilakukan agar mengetahui hasil perbaikan kualitas dari citra yang diberi noise. Output yang akan dihasilkan dalam tugas akhir ini yaitu penulis membahas bagaimana sebuah citra yang awalnya mempunyai kualitas yang baik setelah itu diberi noise, kemudian diterapkan proses filtering dengan menggunakan metode adaptive median filter dan perbaikan kualitas citra dengan menggunakan image enhancement. Kata kunci: AMF, Citra, Hasil, Noise

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M, Suriya Priyadharsini, and G. R. Sathiaseelan J. "The New Robust Adaptive Median Filter for Denoising Cancer Images Using Image Processing Techniques." Indian Journal of Science and Technology 16, no. 35 (2023): 2813–21. https://doi.org/10.17485/IJST/v16i35.1024.

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Abstract Background/Objectives: One of the leading causes of death for women is breast cancer, and extensive research has been conducted to improve the diagnosis and detection of breast cancer using various image processing techniques. Medical imaging plays a crucial role in this domain, particularly mammography, which is widely used for breast cancer screening and diagnosis. This paper introduces a novel filtering technique called the New Robust Adaptive Median Filter (RAMF). Method: The suggested approach only takes into account noise-free pixels when determining the window’s median. The median is computed from the remaining pixel values when the ◦ or 255 pixel values are excluded. In order to filter high densities of salt-andpepper noise, the adaptive windowing approach is applied, which enables our algorithm to extend the size of its filtering window dependent on the local noise density. Moreover, a threshold value is employed to establish the pixel value under extreme circumstances, such as pure black and white photos with noise. Finding: To compare filters based metrics, we find evaluate the filters using a standardized dataset and calculate the MSE, PSNR, and UQI values for each filter. These values can then be compared to determine which filter performs better in terms of noise reduction and image quality enhancement. The Proposed Filter shows good performance in low and higher density ranges (10%-90%) to effectively reduce noise in higher density values. Novelty: The New Robust Adaptive Median Filter (RAMF) is a novel filtering technique that aims to reduce noise in images, particularly in the presence of highly corrupted or noisy pixels. This filtering algorithm employs an adaptive approach where the median is calculated in a processing window, but without considering the noisy pixels during the initial computation. Keywords: Adaptive Median Filter (AMF); Weighted Median Filter (WMF); Noise Adaptive Fuzzy Switching Median (NAFSM) filter; Decision-based algorithm (DBA); Mean Square Error (MSE); Peak-Signal-to-Noise Ratio (PSNR) and Universal Quality Index (UQI)

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Komal, Rani* Gaurav Banga. "DENOISING TECHNIQUE USING TRIMMED BILATERAL FILTERING METHOD." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 9 (2016): 396–404. https://doi.org/10.5281/zenodo.154221.

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This paper work presents an trimmed median filter Bilateral algorithms for the removal of impulse noise has been proposed with color images by separation red- green- blue plane of color image. This proposed algorithm shows better results than the Standard Median Filter (MF), Decision Based Algorithm (DBA), Adaptive Median Filter(AMF) , Hybrid Filter Based Algorithm (HMF), and Trimmed Median Filter (TMF). The performance of the system is analyzed in conditions of Mean square error (MSE), Peak signal to noise ratio (PSNR) image enhancement factor (IEF) and time required for executing the algorithms for different noise density

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Wu, Shixiao, Chengcheng Guo, and Xinghuan Wang. "Application of Principal Component Analysis and Adaptive Median Filter to Improve Real-Time Prostate Capsula Detection." Journal of Medical Imaging and Health Informatics 10, no. 2 (2020): 336–47. http://dx.doi.org/10.1166/jmihi.2020.2883.

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This study investigated the potential for using Principal Component Analysis (PCA) and Adaptive Median Filter (AMF) to improve real-time prostate capsula detection with the traditional Region-based Fully Convolutional Network (R-FCN), Faster Region-based Convolutional Neural Network (Faster R-CNN), You Only Look Once-Version 3 (YOLOv3) and Single Shot Multibox Detector (SSD) algorithms. The processing steps included data augmentation (rotation, vertical flip, and horizontal flip) to increase the size of the dataset from 149 to 596 images, PCA-based feature extraction, AMF-based image denoising and a training phase incorporating the sample image set. The data were then used to test a series of combined methods that were applied to the detection of prostate capsula (PC). The results showed that application of PCA and AMF to Faster R-CNN increased the mean average precision (mAP) for the PC images by 9.4%. The application of PCA and AMF to R-FCN, YOLOv3 and SSD increased the mAP by 7.22%, 7.14% and 3.29% for the same dataset, respectively. This study represents the first application of PCA and AMF to traditional object detection algorithms, such as R-FCN, Faster R-CNN, YOLOv3, or SSD, and the improved mAP results suggest that this approach is a robust tool for improving multiple network architectures.

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Patanavijit, Vorapoj, and Kornkamol Thakulsukanant. "Removal of Fix Magnitude Impulsive Noise (FMIN) Through Innovative Recursive MDBUTMF Procedure." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 18, no. 4 (2024): 494–507. http://dx.doi.org/10.37936/ecti-cit.2024184.255610.

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This article proposes an innovative recursive modied decision based unsymmetrical trimmed median filter (RMDBUTMF) procedure for noisy overriding of digital photographs, which are eminently contaminated by FMIN. The proposed procedure reinstates the noisy photographical basis (which has magnitude at 0 or 255) by trimmed median magnitude (or the mean magnitude of all the free-noise photographical basis) in the computational photographical basis region under the recursive framework. The proposed procedure is experimented on distinctive digital photographs (Lena, Girl, Pepper and F16) on broad noise density and the proposed procedure reveals superior noisy-overridden photographs than the Mean Filter (MF), Median Filter (SMF), Adaptive Median Filter (AMF), Weight Median Filter (WMF), MDBUTMF in both Peak Signal-to-Noise Ratio (PSNR) and photographical quality.

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Patanavijit, Vorapoj, and Kornkamol Thakulsukanant. "A comprehensive achievement investigation of iterative mean filter for outlier extinguish aspiration on ubiquitous FVIN." Bulletin of Electrical Engineering and Informatics 13, no. 2 (2024): 1007–14. http://dx.doi.org/10.11591/eei.v13i2.5951.

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Under commonwealth of the outlier extinguish inspection, exclusively on the impulsive outlier, the outlier extinguish algorithm is a substantial step, which is early performed prior to further computer vision steps thereupon the iterative mean filter (IMF) is inaugurated for fix value impulsive noise (FVIN) and grown into one of the superior achievement outliers extinguish algorithms. This academic article focuses to investigate the correlative achievement of the outlier extinguish algorithm established on IMF, is inaugurated from mean filter (MF) for carrying out the poor achievement of the aforesaid outlier extinguish algorithms (standard median filter (SMF), MF, and adaptive median filter (AMF)), for FVIN at omnipresent scattering of outlier consistency (5-90%). The analytical experiment comprehensively exploits on bountiful figures (F16, Girl, Lena, and Pepper) that are inspected in order to analyze the correlative achievement of an outlier extinguish algorithm established on IMF. In contrast with the aforesaid outlier extinguish algorithms (SMF, MF, and AMF), the outlier extinguish algorithm established on IMF has superior achievement from the experimental results.

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Vorapoj, Patanavijit Kornkamol Thakulsukanant. "Deportation of constant amplitude impulsive outlier (CAIO) through novel repetitive new switching-based median filtering approach." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 2 (2025): 789–800. https://doi.org/10.11591/ijeecs.v37.i2.pp789-800.

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This research paper nominates a novel repetitive new switching-based median filtering approach (R-NSBMF) for outlier deportation on computer numerical pictures that are surpassingly subverted by constant amplitude impulsive outlier (CAIO) or Salt & Pepper noise. This approach reestablishes the outlier numerical pictorial feature (which has the minimum amplitude or the maximum amplitude) by the median filter of the finite impulse response (FIR) linear predictor of all the non-outlier numerical pictorial feature in the calculating numerical pictorial division under the repetitive groundwork. The proposed R-NSBMF approach is investigated on numerous computer numerical pictures (Girl, Lena, Pepper and F16) on spacious outlier percentage and the proposed R-NSBMF approach exposes admirable outlier-deportation numerical pictures than the mean filter (mf), standard median filter (SMF), adaptive median filter (AMF), weight median filter (WMF) and original NSBMF and it professes admirable peak signal-to-noise ratio (PSNR) and pictorial quality.

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Patanavijit, Vorapoj, and Kornkamol Thakulsukanant. "Deportation of constant amplitude impulsive outlier (CAIO) through novel repetitive new switching-based median filtering approach." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 2 (2025): 789. http://dx.doi.org/10.11591/ijeecs.v37.i2.pp789-800.

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This research paper nominates a novel repetitive new switching-based median filtering approach (R-NSBMF) for outlier deportation on computer numerical pictures that are surpassingly subverted by constant amplitude impulsive outlier (CAIO) or Salt &amp; Pepper noise. This approach reestablishes the outlier numerical pictorial feature (which has the minimum amplitude or the maximum amplitude) by the median filter of the finite impulse response (FIR) linear predictor of all the non-outlier numerical pictorial feature in the calculating numerical pictorial division under the repetitive groundwork. The proposed R-NSBMF approach is investigated on numerous computer numerical pictures (Girl, Lena, Pepper and F16) on spacious outlier percentage and the proposed R-NSBMF approach exposes admirable outlier-deportation numerical pictures than the mean filter (mf), standard median filter (SMF), adaptive median filter (AMF), weight median filter (WMF) and original NSBMF and it professes admirable peak signal-to-noise ratio (PSNR) and pictorial quality.

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Patanavijit, Vorapoj. "Computational scrutiny of image denoising method found on DBAMF under SPN surrounding." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 4109. http://dx.doi.org/10.11591/ijece.v10i4.pp4109-4117.

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Traditionally, rank order absolute difference (ROAD) has a great similarity capacity for identifying whether the pixel is SPN or noiseless because statistical characteristic of ROAD is desired for a noise identifying objective. As a result, the decision based adaptive median filter (DBAMF) that is found on ROAD technique has been initially proposed for eliminating an impulsive noise since 2010. Consequently, this analyzed report focuses to examine the similarity capacity of denoising method found on DBAMF for diverse SPN Surrounding. In order to examine the denoising capacity and its obstruction of the denoising method found on DBAMF, the four original digital images, comprised of Airplane, Pepper, Girl and Lena, are examined in these computational simulation for SPN surrounding by initially contaminating the SPN with diverse intensity. Later, all contaminated digital images are denoised by the denoising method found on DBAMF. In addition, the proposed denoised image, which is computed by this DBAMF denoising method, is confronted with the other denoised images, which is computed by Standard median filter (SMF), Gaussian Filter and Adaptive median filter (AMF) for demonstrating the DBAMF capacity under subjective measurement aspect.

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Vorapoj, Patanavijit. "Computational scrutiny of image denoising method found on DBAMF under SPN surrounding." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 4109–17. https://doi.org/10.11591/ijece.v10i4.pp4109-4117.

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Traditionally, rank order absolute difference (ROAD) has a great similarity capacity for identifying whether the pixel is SPN or noiseless because statistical characteristic of ROAD is desired for a noise identifying objective. As a result, the decision based adaptive median filter (DBAMF) that is found on ROAD technique has been initially proposed for eliminating an impulsive noise since 2010. Consequently, this analyzed report focuses to examine the similarity capacity of denoising method found on DBAMF for diverse SPN Surrounding. In order to examine the denoising capacity and its obstruction of the denoising method found on DBAMF, the four original digital images, comprised of Airplane, Pepper, Girl and Lena, are examined in these computational simulations for SPN surrounding by initially contaminating the SPN with diverse intensity. Later, all contaminated digital images are denoised by the denoising method found on DBAMF. In addition, the proposed denoised image, which is computed by this DBAMF denoising method, is confronted with the other denoised images, which is computed by standard median filter (SMF), gaussian filter and adaptive median filter (AMF) for demonstrating the DBAMF capacity under subjective measurement aspect.

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Jabbar, Hawraz N., Yoksal A. Laylani, Issam A. R. Moghrabi, and Basim A. Hassan. "Development of a New Numerical Conjugate Gradient Technique for Image Processing." WSEAS TRANSACTIONS ON COMPUTER RESEARCH 12 (November 13, 2023): 123–31. http://dx.doi.org/10.37394/232018.2024.12.12.

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We present a new iterative conjugate gradient technique for image processing. The technique is based on a new derivation of the conjugacy coefficient and develops a variant of the classical Fletcher-Reeves conjugate gradient method. The derivation exploits a quadratic function model. The new method is intended to minimize the presence of noise by utilizing the adaptive median filter (AMF) to reduce salt-and-pepper noise, while the adaptive center-weighted median filter (ACWMF) is used to reduce random-valued noise. The theoretical convergence properties of the method are proven and then tested on a basic set of images using MATLAB. The results show that the proposed algorithm is more efficient than the classical Fletcher-Reeves (FR) method, as measured by the signal-to-noise ratio (PSNR). The number of iterations and the number of function evaluations are also lower for the proposed method. The favorable performance of the new algorithm provides promise for deriving similar techniques that enhance the speed and efficiency of image-processing libraries.

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Zhang, Xin-Ming, Qiang Kang, Jin-Feng Cheng, and Xia Wang. "Adaptive Four-dot Median Filter for Removing 1-99% Densities of Salt-and-Pepper Noise in Images." Journal of Information Technology Research 11, no. 3 (2018): 47–61. http://dx.doi.org/10.4018/jitr.2018070104.

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In order to accelerate denoising and improve the denoising performance of the current median filters, an Adaptive Four-dot Median Filter (AFMF) for image restoration is proposed in this article. AFMF is not only very efficient and fast in logic execution, but also it can restore the corrupted images with 1–99% densities of salt-and-pepper noise to the satisfactory ones. Without any complicated operation for noise detection, it intuitively and simply distinguishes impulse noises, while keeping the noise-free pixels intact. Only the uncorrupted pixels of the four-dot mask in adaptive filtering windows are used for the adoption of candidates for median finding, whatever filtering window size is. Furthermore, the adoption of recursive median filters leads to denoising performance improvement and faster filtering. The simple logic of the proposed algorithm obtains significant milestones on the fidelity of a restored image. Relevant experimental results on subjective visualization and objective digital measure validate the robustness of the proposed filter.

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Wang, Hai Qiang, and Xian Ge Sun. "The Median Filtering of Digital Image Based on MATLAB." Applied Mechanics and Materials 318 (May 2013): 67–70. http://dx.doi.org/10.4028/www.scientific.net/amm.318.67.

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In the processing of image de-noising, median filtering is a more common nonlinear filtering technique. In this paper, we used the image processing toolbox in the matlab and the adaptive filter program by ourselves. The noise in different intensity of image is reduced. This paper discussed the problem that how we choice of the filtering method under different noise intensity. The results show that median filter can realize effective filter under low noise intensity, if the noise intensity is excessively large, the adaptive filter having a more ideal filtering result than that of standard median filter.

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Maharnisha, Gandla, R. Veerasundari, Gandla Roopesh Kumar, and Arunraj . "Improving the Spatial Resolution of Real Time Satellite Image Fusion Using 2D Curvelet Transform." International Journal of Engineering & Technology 7, no. 2.19 (2018): 55. http://dx.doi.org/10.14419/ijet.v7i2.19.15047.

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The fused image will have structural details of the higher spatial resolution panchromatic images as well as rich spectral information from the multispectral images. Before fusion, Mean adjustment algorithm of Adaptive Median Filter (AMF) and Hybrid Enhancer (combination of AMF and Contrast Limited Adaptive Histogram Equalization (CLAHE)) are used in the pre-processing. Here, conventional Principal Component image fusion method will be compared with newly modified Curvelet transform image fusion method. Principal Component fusion technique will improve the spatial resolution but it may produce spectral degradation in the output image. To overcome the spectral degradation, Curvelet transform fusion methods can be used. Curvelet transform uses curve which represents edges and extraction of the detailed information from the image. Curvelet Transform of individual acquired low-frequency approximate component of PAN image and high-frequency detail components from PAN and MS image is used. Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE) are measured to evaluate the image fusion accuracy.

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He, Ping, Hong Jian Zhang, Chao Liu, and Yuan Guo. "An Improved Method of Adaptive Median Filter Based on Noise Density." Applied Mechanics and Materials 530-531 (February 2014): 403–6. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.403.

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To filter salt and pepper noise and protect the texture details of images effectively, an improved method of adaptive median filter is proposed. It can detect the suspicious noise by adjusting the filter window size and adopting the filter algorithm of adaptive texture direction in low density noise area and the filter algorithm of euclidean distance weighted average in high density noise area. Experimental results show that this method has better de-noising and detail-preserving performance.

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J. Kadhim, Sattar, Raheem K. Al-Sabur, and Abdul Baki K. Ali. "Application of Different Median Filter Algorithms for Welding Defects Clarification in Radiographic Images." University of Thi-Qar Journal for Engineering Sciences 11, no. 1 (2020): 56–61. http://dx.doi.org/10.31663/tqujes.11.1.378(2020).

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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

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Patanavijit, Vorapoj, Darun Kesrarat, and Kornkamol Thakulsukanant. "The alternative irregularity reduction algorithm built on 2-stage identification with AMF on FMIO." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (2022): 1518–29. https://doi.org/10.11591/ijeecs.v28.i3.pp1518-1529.

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Built on neighborhood correlation, a 2-stage identification technique was offered to incorporate on the irregularity reduction algorithm for random magnitude impulse outlier (RMIO). As a result, this algorithm has an ultimate efficacy thereby a 2-stage identification technique becomes to be one of the high efficacy identification technique. Accordingly, this paper attempts to propose an alternative irregularity reduction algorithm built on a 2-stage identification and adaptive median filter (AMF) with under fix magnitude impulsive outlier (FMIO) at little, mild and immense massiveness. First, by examining great number of depictions, the optimized window dimension for 2-stage scheme from computation and performance is disposed. Second, comprehensive examinations represent that the 2-stage identification technique is disposed to identify between regular and irregular pixels at all massiveness, especially little and mild massiveness. Third, the identification efficacy on great depictions at all massiveness is examined on regular, irregular, regular-irregular efficacy perspective to estimate the optimal window size and optimal 2-stage constant value. Finally, the overall outlier reduction efficacy of an outlier reduction built on 2-stage technique and AMF is examined on great depictions at all massiveness related with other up-to-the-minute outlier reductions. From these results, the outlier reduction has remarkable efficacy than other up-to-the-minute outlier reductions.

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Kesrarat, Darun, Vorapoj Patanavijit, Kanabadee Srisomboon, Wilaiporn Lee, and Kornkamol Thakulsukanant. "A Pervasive Numerical Investigation of HDF Discrimination for Identifying and Enhancing Impulsive Noise Photograph." Journal of Lifestyle and SDGs Review 5, no. 3 (2025): e03239. https://doi.org/10.47172/2965-730x.sdgsreview.v5.n03.pe03239.

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Objective: This study aims to investigate the optimal cluster size and the optimal HDT parameter of the noise elimination approach founded on HDT (Hard Decision Threshold) discrimination on a lot total of experimental photographs on CTII for maximum capability. Theoretical Framework: The theoretical framework is founded on the dissimilarity values of the cluster pixels are significantly different neighborhood pixels. However, the capability of the noise elimination approach founded on HDT ultimately depends on the optimal cluster size and the optimal HDT parameter. Method: The noise elimination approach founded on HDT is investigated in both quantitative (in PSNR or Peak Signal to Noise Ratio) and qualitative (visionary). This experiment investigates on a lot of photographs (Lena, Pepper and Pentagon) in CTII (Constant Tension Impulsive Irregularity) at uniform distribution and cluster distribution. Results and Discussion: First, the results of the study based on HDT parameters from Lena, Pepper and Pentagon shows that the optimal HDT parameter is 0.2±0.1 approximately. Next, from numerical outcome on a lot of photographs, the HDT noise elimination approach is capable of the remarkable quality photographs with the first-rate PSNR opposed with former reputable approaches for example Gaussian/Mean filter (MF), Median filter (SMF) and Adaptive Median filter (AMF) for wholly CTII.

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Yuan, Pei Xin, Cong Cong Zhang, and Yue Yuan. "Research on Welding Line Defect Recognition of the In-Service Pipeline Using X-Ray Detecting." Applied Mechanics and Materials 195-196 (August 2012): 5–12. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.5.

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This paper, based on the practical demands of in-service pipeline detection, a set of X-ray digital image welding line defect intelligent recognition system is established. Taking the welding line image detected by X-ray as objects of study, self-adaptive median filter method filters noise, high frequency enhancement filter method conducts the image edge sharpening enhancement; a edge detection method for X-ray digital image based on morphological gradient is proposed; a group of characteristics parameters that accurately reflects the essence characteristic of defects is selected, using a self-organizing, self-adaptive three-layer feed-forward neural network, applying BP algorithm, the BP neural network recognition system is established, thus, to achieve detection and recognition of weld defects.

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Pardosi, Irpan Adiputra, and Ali Akbar Lubis. "Analisis Kualitas Citra Hasil Reduksi Noise Menggunakan Spatial Median Filter dan Adaptive Fuzzy Filter Terhadap Variasi Kedalaman Citra." Indonesian Journal of Information Systems 1, no. 2 (2019): 78. http://dx.doi.org/10.24002/ijis.v1i2.1939.

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Algoritma reduksi noise salt pada citra mampu mengurangi sebagian atau keseluruhan noise, tapi berdampak pada keragaman informasi dan kualitas citra. Persentase noise yang lebih besar juga membuat perubahan yang besar pada citra, namun hasilnya dapat berbeda untuk citra dengan kedalaman bit yang berbeda-beda. Kemampuan algoritma reduksi noise mampu bekerja maksimal untuk noise di bawah 20%. Penelitian terdahulu mengenai proses reduksi noise citra diantaranya menggunakan algoritma Adaptive Fuzzy Filter (AFF) dan Spatial Median Filter (SMF) yang mampu menghilangkan noise. Keduanya mampu mereduksi noise dengan hasil maksimal di bawah 45% pada citra 8 bit, namun menyisakan beberapa noise. Oleh karena itu, perlu dikaji kinerja algoritma dan dampaknya terhadap citra dengan noise yang lebih besar. Penelitian ini khusus mengatasi noise jenis salt and pepper dengan persentase noise di atas 45% pada citra warna bitmap. Selain itu, penelitian ini menganalisis citra hasil mulai dari kualitas citra dan keragaman informasi setelah proses reduksi noise dengan menggunakan Algoritma SMF dan AFF. Dari hasil pengujian citra untuk persentase noise salt 45%, 55%, 65%, dan 75% pada kedalaman citra 8, 16, dan 24 bit, dapat disimpulkan bahwa Algoritma AFF lebih baik dibandingkan SMF mengacu pada nilai Peak Signal to Noise Ratio (PSNR), sebaliknya algoritma SMF lebih baik untuk keragaman informasi, mengacu pada nilai shannon entropy. Kedua hal ini berlaku untuk semua variasi kedalaman citra warna.

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Patanavijit, Vorapoj, Darun Kesrarat, and Kornkamol Thakulsukanant. "The alternative irregularity reduction algorithm built on 2-stage identification with AMF on FMIO." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (2022): 1518. http://dx.doi.org/10.11591/ijeecs.v28.i3.pp1518-1529.

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Chen, Xue Fang, Hao Bing Guan, Ji Nan Gu, and Qian Wu. "A Study and Improvements on Canny Algorithm." Advanced Engineering Forum 6-7 (September 2012): 205–9. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.205.

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This paper studies principle of the traditional Canny edge detection algorithm and the existing problems. Some solutions toward the problems are also proposed. The improvement methods consisted of two parts. On the one hand, as Canny edge algorithm itself does not have strong noise immunity while ensuring edge positioning accuracy, an adaptive median filter denoising program based on the Canny Gaussian filter is proposed to enhance the Canny algorithm filter performance. On the other hand, given the Canny algorithm high and low threshold parameter is not determined by the feature information of the image edge, but rather set manually which does not have the adaptive ability, this paper proposes an adaptive threshold setting scheme for high and low thresholds to improve the adaptive ability of Canny algorithm to different images.

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M. D., Rakshith, and Harish H. Kenchannavar. "Hybrid Deep Optimal Network for Recognizing Emotions Using Facial Expressions at Real Time." International Journal of Intelligent Systems and Applications 16, no. 3 (2024): 47–58. http://dx.doi.org/10.5815/ijisa.2024.03.04.

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Recognition of emotions by utilizing facial expressions is the progression of determining the various human facial emotions to infer the mental condition of the person. This recognition structure has been employed in several fields but more commonly applied in medical arena to determine psychological health problems. In this research work, a new hybrid model is projected using deep learning to recognize and classify facial expressions into seven emotions. Primarily, the facial image data is obtained from the datasets and subjected to pre-processing using adaptive median filter (AMF). Then, the features are extracted and facial emotions are classified through the improved VGG16+Aquila_BiLSTM (iVABL) deep optimal network. The proposed iVABL model provides accuracy of 95.63%, 96.61% and 95.58% on KDEF, JAFFE and Facial Expression Research Group 2D Database (FERG-DB) which is higher when compared to DCNN, DBN, Inception-V3, R-152 and Convolutional Bi-LSTM models. The iVABL model also takes less time to recognize the emotion from the facial image compared to the existing models.

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Kalaimani, G., K. Manojkumar, and Sathish S. Kumar. "Median Filtering for Removal of Maximum Impulse Noise from Images with a Decision Based Model." Journal of Computational and Theoretical Nanoscience 16, no. 2 (2019): 562–67. http://dx.doi.org/10.1166/jctn.2019.7769.

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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.

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Qiao, Zhong Tao, Feng Qi Gao, Guang Long Wang, and Liang Liang Chang. "A Denoising Mixed Noise Method Based on Median Filter and Lifting Wavelet Technology." Applied Mechanics and Materials 411-414 (September 2013): 1546–51. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1546.

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In image digitization and transmission, images often suffer contamination inevitably. The noises in images often consist of Gaussian noise and impulse noise. The common denoising algorithms are capable of removing single one of them. In order to remove those two types of noise, a composite algorithm is proposed. Firstly, based on median filter, an impulse noise detection algorithm is used to filter impulse noise. Secondly, adaptive directional lifting wavelet (ADL) and normal lifting wavelet is combined to suppress noise from image signal and protect the texture edge from loss simultaneously. Meanwhile an improved half-soft threshold is used for normal lifting wavelet. At last, simulations show that this technology can suppress Gaussian and impulse noise in image efficiently．

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Hassan, Basim A., Hawraz N. Jabbar, Yoksal A. Laylani, Issam Abdul Rahman Moghrabi, and Ali Joma Alissa. "An enhanced fletcher-reeves-like conjugate gradient methods for image restoration." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6268. http://dx.doi.org/10.11591/ijece.v13i6.pp6268-6276.

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Noise is an unavoidable aspect of modern camera technology, causing a decline in the overall visual quality of the images. Efforts are underway to diminish noise without compromising essential image features like edges, corners, and other intricate structures. Numerous techniques have already been suggested by many researchers for noise reduction, each with its unique set of benefits and drawbacks. Denoising images is a basic challenge in image processing. We describe a two-phase approach for removing impulse noise in this study. The adaptive median filter (AMF) for salt-and-pepper noise identifies noise candidates in the first phase. The second step minimizes an edge-preserving regularization function using a novel hybrid conjugate gradient approach. To generate the new improved search direction, the new algorithm takes advantage of two well-known successful conjugate gradient techniques. The descent property and global convergence are proven for the new methods. The obtained numerical results reveal that, when applied to image restoration, the new algorithms are superior to the classical fletcher reeves (FR) method in the same domain in terms of maintaining image quality and efficiency.

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Basim, A. Hassan, N. Jabbar Hawraz, Abdul Rahman Moghrabi Issam, and Joma Alissa Ali. "An enhanced fletcher-reeves-like conjugate gradient methods for image restoration." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6268–76. https://doi.org/10.11591/ijece.v13i6.pp6268-6276.

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Pardosi, Irpan Adiputra, and Ali Akbar Lubis. "Grayscale Image Quality Analysis Result of Noises Reduction using Adaptive Fuzzy Filter (AFF) and Spatial Median Filter (SMF) Against Image Depth Variations." Journal of Physics: Conference Series 1361 (November 2019): 012024. http://dx.doi.org/10.1088/1742-6596/1361/1/012024.

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Ojha, Gajanand, Aishwary Awasthi, and Vinita Gupta. "Novel machine learning method in skin cancer diagnosis based on patient's metadata." Multidisciplinary Science Journal 5 (September 5, 2023): 2023ss0405. http://dx.doi.org/10.31893/multiscience.2023ss0405.

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The skin's fundamental function in human body as a whole-body covering is crucial. Only if it is discovered while it is in its early stages can skin cancer be cured. Skin function plays a big part in the body's overall system and will be significantly impacted by even the slightest modification. The goal of this work was to develop an effective Machine Learning (ML) based technique for identification of skin cancer using patient information. To diagnose skin cancer with lesions image, this research introduces a novel Augmented May Fly optimized with K-Nearest Neighbors (AMFO-KNN) technique. Here, the AMFO approach is used to improve the classification efficiency of KNN. Utilizing the PAD-UFES-20 and Fitzpatrick17k datasets, the efficiency of suggested method is examined. The noisy data are removed from the raw data samples using Adaptive Median Filter (AMF). The properties are taken out of segmented data using Kernel Principal Component Analysis (KPCA). The performance metrics of research show that recommended methodology performs better than traditional approaches in terms of accuracy, precision, f1-score, and recall measures. The encouraging results demonstrate the effectiveness of suggested strategy and show that including the patient's information with lesions image may improve the performance of skin cancer diagnosis.

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Oyovwe, B. E., A. E. Edje, E. Omede, and C. Ogeh. "An enhanced Convolutional Neural Network (CNN) model for the detection of lung cancer using X-Ray image." Scientia Africana 23, no. 2 (2024): 321–30. http://dx.doi.org/10.4314/sa.v23i2.29.

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Lung Cancer is a life-threatening disease which can be diagnosed by Medical Imaging such as X-Ray, MRI, CT Scan etc. This research presented an enhanced model using Convolutional Neural Network (CNN) to detect lung cancer using X-Ray image. Medical image processing relies heavily on the diagnosis of lung cancer images. It aids doctors in determining the correct diagnosis and management. For many patients, lung cancer ranks among the mostdeadly diseases. Many lives can be saved if cancerous growth is diagnosed early. The purported model was predominantly built on Convolutional Neural Network (CNN) architecture and the model was built with enhanced features such as Image Enhancement, Segmenting ROI (Region of Interest), Features Extraction and Nodule Classification. In preprocessing stage, the AMF (Adaptive Median Filter) filtering method was applied to eliminate noise in X-Ray image of the dataset, and quality of X-Ray image was improved with the support of CLAHE (Contrast Limited Adaptive Histogram Equalization). Secondly, K-means Clustering algorithm was used to extract the relevant feature or Region of Interest (ROI) of the lung field automatically i.e. the model was effectively trained to identify and crop the exact location of the lung field automatically. The model was able to classify the cancer nodule as either Cancerous or Non-Cancerous. The framework worked on C# platform, and used EMGU for detection of the tumour in the lung xray image. Experimental result showed that the developed system was able to detect Lung Cancer with 90.77% accuracy, 86.65% precision and 95.31% Recall/Sensitivity.

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Weng, Li Yuan, Min Li, and Zhen Bang Gong. "On Sonar Image Processing Techniques for Detection and Localization of Underwater Objects." Applied Mechanics and Materials 236-237 (November 2012): 509–14. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.509.

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This paper presents an underwater object detection and localization system based on multi-beam sonar image processing techniques. Firstly, sonar data flow collected by multi-beam sonar is processed by median filter to reduce noise. Secondly, an improved adaptive thresholding method based on Otsu method is proposed to extract foreground objects from sonar image. Finally, the object’s contour is calculated by Moore-Neighbor Tracing algorithm to locate the object. Experiments show that the proposed system can detect underwater objects quickly and the figure out the position of objects accurately.

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Xie, Juanhong, Guojian Shi, and Weizhi Zhu. "Intelligent Recognition Technology for the Segmentation of Traffic Indication Images Concerning Different Pavement Materials." Applied Bionics and Biomechanics 2022 (September 20, 2022): 1–7. http://dx.doi.org/10.1155/2022/6278240.

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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%.

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Yan, Kang, and Zhong Yuan Zhang. "Application of Improved Back Propagation Neural Network for the Recognition of Composite Insulator Hydrophobicity Grade." Applied Mechanics and Materials 373-375 (August 2013): 1155–58. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1155.

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The detection of hydrophobicity is an important way to evaluate the performance of composite insulator, which is helpful to the safe operation of composite insulator. In this paper, the image processing technology and Back Propagation neural network is introduced to recognize the composite insulator hydrophobicity grade. First, hydrophobic image is preprocessed by histogram equalization and adaptive median filter, then the image was segmented by Ostu threshold method, and four features associated with hydrophobicity are extracted. Finally, the improved Back Propagation neural network is adopted to recognize composite insulator hydrophobicity grade. The experimental results show that the improved Back Propagation neural network can accurately recognize the composite insulator hydrophobicity

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Zhang, Ying Jin, Shi Yin Qin, and Xiao Hui Hu. "Fast Algorithms of Multi-Object Recognition and High Precision Localization for Pose Estimation." Applied Mechanics and Materials 333-335 (July 2013): 1192–97. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1192.

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The recognition and localization of cooperative objects are very important for spacecraft pose estimation towards autonomous rendezvous and docking (RVD). In this paper, an adaptive threshold segmentation algorithm is proposed base on weighted maximum gray value for multi-object detection, and eight-neighborhood region growing is employed for multi-object recognition. In order to achieve high-accurate localization, a sub-pixel object positioning approach is put forward by combination bilinear interpolation with median filtering, which employs bilinear interpolation to calculate sub-pixel centroid for reducing algorithm systematic errors, and applies median filter to reduce random errors produced by image noises. The experimental results show that the proposed algorithms are feasible and effective with high positioning accuracy of 0.01 pixels, and have outstanding advantages of anti-disturbance and real-time performance, thus can satisfy the practical requirements in the visual measurement and pose estimation of cooperative objects for the RVD in space exploration.

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Dong, Li Hong, and Peng Bing Zhao. "Application of Improved Canny Edge Detection Algorithm in Coal-Rock Interface Recognition." Applied Mechanics and Materials 220-223 (November 2012): 1279–83. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1279.

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The coal-rock interface recognition is one of the critical automated technologies in the fully mechanized mining face. The poor working conditions underground result in the seriously polluted edge information of the coal-rock interface, which affects the positioning precision of the shearer drum. The Gaussian filter parameters and the high-low thresholds are difficult to select in the traditional Canny algorithm, which causes the information loss of gradual edge and the phenomenon of false edge. Consequently, this paper presents an improved Canny edge detection algorithm, which adopts the adaptive median filtering algorithm to calculate the thresholds of Canny algorithm according to the grayscale mean and variance mean. This algorithm can protect the image edge details better and can restrain the blurred image edge. Experimental results show that this algorithm has improved the edge extraction effect under the case of noise interference and improved the detection precision and accuracy of the coal-rock image effectively.

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Yang, Yan Zhu, Wei Liang Liu, Neng Jie Chen, and Wei Zhu. "A Mixed Noise Filtering Algorithm for High-Speed Sequence Image Processing." Applied Mechanics and Materials 415 (September 2013): 318–24. http://dx.doi.org/10.4028/www.scientific.net/amm.415.318.

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t is a effective means by using high-speed vision to locate mobile targets. Under the circumstance of high frame rate and high sensitivity (300Hz), in addition to the Gaussian noise and impulse noise, the image quality is also influenced by atmospheric instability, and it is mainly expressed as Gaussian noise. An improved adaptive threshold weighted mean (IATWM) de-noising algorithm is proposed in this paper. According to the characteristics of impulse noise, the algorithm is able to obtain the threshold adaptively and separate the impulse noise. Then, the weighted median filtering algorithm is used to remove the impulse noise. And the improved weighted average filter algorithm is adopted to remove the Gaussian noise for graphics with Gaussian noise. The algorithm could deal with the Gaussian noise and impulse noise separately, avoiding the weaken handling for the parts not subject to pixels pollution of the impulse noise. The experimental results show that the processing result of the algorithm is able to retain the image details, superior to the traditional filtering algorithms and MTM algorithm. In addition, the algorithm provides an effective way to eliminate the mixed noise, along with a good effect on the high-speed sequence image processing.

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Li, Jun, Hongchao Wang, Simin Li, Liang Chen, and Qiqian Dang. "A Two-Stage Rolling Bearing Weak Fault Feature Extraction Method Combining Adaptive Morphological Filter with Frequency Band Selection Strategy." Applied Sciences 13, no. 1 (2023): 668. http://dx.doi.org/10.3390/app13010668.

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To extract the weak fault features hidden in strong background interference in the event of the early failure of rolling bearings, a two-stage based method is proposed. The broadband noise elimination ability of an adaptive morphological filter (AMF) and the superior capability of a frequency band selection (FBS) strategy for fault transient location identification are comprehensively utilized by the proposed method. Firstly, the AMF with a simple theory and high calculation efficiency is used as a preprocessing program to enhance the fault transient features. Then, the proposed FBS strategy based on the sparsity index (SI) is utilized to further handle the filtered signal processed by the AMF. Finally, the constructed optimum bandpass filter based on the analysis result of the FBS is used to further filter the handled signal processed by AMF and envelope spectral analysis is applied on the last filtered signal to realize the ideal fault feature extraction effect. Compared with the other traditional FBS methods based on kurtosis or the other index, the proposed FBS strategy based on SI has strong robustness to noise. One experimental signal and one engineering vibration signal are used, respectively, to verify the feasibility of the proposed method.

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Xin Wang. "Adaptive multistage median filter." IEEE Transactions on Signal Processing 40, no. 4 (1992): 1015–17. http://dx.doi.org/10.1109/78.127983.

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Ali, Nawafil Abdulwahab, and Imad Al Shaikhli. "Proposed De-noising Algorithm." International Journal on Perceptive and Cognitive Computing 6, no. 2 (2020): 90–96. http://dx.doi.org/10.31436/ijpcc.v6i2.164.

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minimizing noises from images to restore it and increase its quality is a crucial step. For this, an efficient algorithms were proposed to remove noises such as (salt pepper, Gaussian, and speckle) noises from grayscale images. The algorithm did that by selecting a window measuring 3x3 as the center of processing pixels, other algorithms did that by using median filter (MF), adopted median filter (AMF), adopted weighted filter (AWF), and the adopted weighted median filter (AWMF). The results showed that the proposed algorithm compares to previous algorithms by having a better signal-to-noise ratio (PSNR).

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Gao, Yong Li. "Filter Window Characteristics Based Fast Adaptive Median Filter." Advanced Materials Research 487 (March 2012): 7–10. http://dx.doi.org/10.4028/www.scientific.net/amr.487.7.

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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.

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Xiong, Caiquan. "Selective adaptive weighted median filter." Optical Engineering 47, no. 3 (2008): 037001. http://dx.doi.org/10.1117/1.2891297.

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Xiahua Yang and Peng Seng Toh. "Adaptive fuzzy multilevel median filter." IEEE Transactions on Image Processing 4, no. 5 (1995): 680–82. http://dx.doi.org/10.1109/83.382502.

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Nalli, Praveen Kumar, Kalyan Sagar Kadali, Ramu Bhukya, Y. T. R. Palleswari, Asapu Siva, and S. Pragaspathy. "Design of Exponentially Weighted Median Filter Cascaded With Adaptive Median Filter." Journal of Physics: Conference Series 2089, no. 1 (2021): 012020. http://dx.doi.org/10.1088/1742-6596/2089/1/012020.

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Abstract The objective of this paper is to design an II phase algorithm employing median filters for enlightening the performance in removing impulse noise during the processing of the image. The cascaded filter section employs an Adaptive median filter in the first phase followed by a Recursive weighted median filter (RWM) in the second phase. The RWM filter weight is selected with the Median Controlled Algorithm. As a design parameter, the exponential weights of RWM filters are used in the feedback path. The projected algorithm can achieve suggestively improved quality of image when compared to fixed weight or the Center Weighted Median filters.

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Nayak, N. R., P. K. Dash, B. N. Sahu, and Ranjeeta Bisoi. "A New Adaptive Multiscale Morphological Filter and Robust RVFLN Classifier for Distributed Generation Systems during Islanding and Non-Islanding Events." International Journal on Electrical Engineering and Informatics 12, no. 3 (2020): 494–518. http://dx.doi.org/10.15676/ijeei.2020.12.3.6.

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A new method for islanding and non-islanding disturbances detection and classification is proposed for a multiple PV based distributed generation (DG) system utilizing adaptive multi-scale morphological filter (AMF) and random vector functional link network (RVFLN) classifier. In comparison to different signal analysis techniques, the mathematical MF, that has wide application in power signals, EEG signal analysis, image processing, pattern recognition, etc. posses the benefit of easy execution, fast processing, and minimal computations. Further it is well known that a single scale morphological filter has limited noise filtering capacity and may also filter useful signal disturbance components resulting in erroneous detection of disturbance signals in microgrid. Therefore, an adaptive multiscale combined morphological filter is presented in this paper built on the concept of multiscale overall filtering which has better denoising effect and can retain useful signals better than the traditional filter. The proposed technique is built upon the measurement of voltage signal samples and the processing of these signals through AMF has been done for feature extraction. The extracted features are then employed as inputs to an efficient, fast, and easily implementable randomized network based classifier (RVFLN) which is made robust to reject the presence noise and outliers in the signal data. The outputs exhibited from the suggested technique concludes that it is s very fast and accurate technique for the detection and classification of islanding and non-islanding events in comparison to the widely used approaches.

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Akhtyrskiy, K. A., V. A. Kabirov, V. D. Semenov, and D. S. Torgaeva. "Multi-channel adaptive median signal filter." iPolytech Journal 28, no. 4 (2025): 504–20. https://doi.org/10.21285/1814-3520-2024-4-504-520.

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This study aims to develop a novel structure of an N-channel adaptive median signal filter with dynamic input exclusion. The proposed design is intended for highly reliable, fault-tolerant modular redundant power supply systems for spacecraft. To investigate the functionality of the proposed N-channel filter, we developed a simulation model for a 7-channel median signal filter using MATLAB Simulink. A model-based design approach was applied to validate the performance of the proposed element using the Altera Cyclone IV EP4CE115F29C7 field-programmable gate array (FPGA). The verification process involved the use of automatic code generation tools within MATLAB Simulink for the FPGA implementation. A novel structure of the N-channel median signal filter was proposed, featuring a dynamic exclusion of unused inputs from the median calculation. This guarantees that only valid input signals from operational modules within the power supply system be included in the median calculation. The simulation results demonstrated that, in contrast to existing counterparts, the proposed filter is capable of reliably outputting the median signal as the number of active input signals decreases from N to 1. The implementation of the filter as an intellectual property (IP) block based on the Altera Cyclone IV EP4CE115F29C7 FPGA demonstrated efficient resource utilisation, occupying 541 logic cells, while fully adhering to the specified operational logic. The proposed structure of the adaptive median signal filter can be employed in highly reliable, fault-tolerant spacecraft redundant power supply systems, maintaining functionality even in the event of multiple module failures, down to the last operational module. The developed solution meets the stringent fault-tolerance requirements of spacecraft power systems.

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Samantaray, Aswini Kumar, and Priyanka Mallick. "Decision Based Adaptive Neighborhood Median Filter." Procedia Computer Science 48 (2015): 222–27. http://dx.doi.org/10.1016/j.procs.2015.04.174.

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How-Lung Eng and Kai-Kuang Ma. "Noise adaptive soft-switching median filter." IEEE Transactions on Image Processing 10, no. 2 (2001): 242–51. http://dx.doi.org/10.1109/83.902289.

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Akkoul, Smaïl, Roger Ledee, Remy Leconge, and Rachid Harba. "A New Adaptive Switching Median Filter." IEEE Signal Processing Letters 17, no. 6 (2010): 587–90. http://dx.doi.org/10.1109/lsp.2010.2048646.

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Zhang, Hongming, Yongping Wang, and Chuang Peng. "Ameliorated mean adaptive median filtering algorithm." E3S Web of Conferences 118 (2019): 02069. http://dx.doi.org/10.1051/e3sconf/201911802069.

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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.

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Zhang, Dai Yuan, Wen Bo Liu, and Zhuo Zhu. "Computationally Efficient Adaptive Sidelobe Blanker with Improved Mismatched Signals Rejection." Applied Mechanics and Materials 644-650 (September 2014): 4372–77. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.4372.

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This paper deals with the problem of adaptive signal detection in the presence of Gaussian noise with unknown covariance matrix. An improved adaptive sidelobe blanker (ASB) is proposed, which consists of an adaptive matched filter (AMF) followed by a whitened adaptive beamformer orthogonal rejection test (W-ABORT). A statistical characterization for the proposed two-stage test statistic is provided,under both noise-only and signal-plus-noise hypotheses. The associated probability of false alarm (Pfa) and probability of detection (Pd) are derived in closed form. The performance assessment confirms the effectiveness of the newly-proposed detection algorithms also in comparison to previously-proposed ones.

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