Two methods of adaptive median filtering of impulse noise in images

Chervyakov, Nikolay I.; Lyakhov, Pavel; Orazaev, A.R.

doi:10.18287/2412-6179-2018-42-4-667-678

Cited by 18 publications

(9 citation statements)

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“…AMF has been widely used as a classic excellent filter for filtering salt and pepper noise [21][22][23][24]. Y i represents the coordinate window of a rectangle of size L * L, and L max * L max is the maximum size of the rectangle window during the algorithm.…”

Section: Denoising Algorithm Based On Nlm Algorithmmentioning

confidence: 99%

Image Noise Removal Method Based on Improved Nonlocal Mean Algorithm

Ke-ya

Zhu²

2021

Complexity

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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.

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Section: Denoising Algorithm Based On Nlm Algorithmmentioning

confidence: 99%

Image Noise Removal Method Based on Improved Nonlocal Mean Algorithm

Ke-ya

Zhu²

2021

Complexity

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“…Paper [13] adopted laser scanning technology to detect overfill and underfill defects on upper surface of the deposited parts in the additive manufacturing process by comparing the existing point cloud with the presliced stereolithography (STL) model. In paper [14], Chervyakov et al utilized two modified adaptive median filtering methods of impulse noise in images. e experiment showed potential application in processing satellite and medical imagery, geophysical data, and in other areas of digital image processing.…”

Section: Related Researchmentioning

confidence: 99%

Industrial Printing Image Defect Detection Using Multi-Edge Feature Fusion Algorithm

et al. 2021

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Online defect detection system is a necessary technical measure and important means for large-scale industrial printing production. It is effective to reduce artificial detection fatigue and improve the accuracy and stability of industry printing line. However, the existing defect detection algorithms are mainly developed based on high-quality database and it is difficult to detect the defects on low-quality printing images. In this paper, we propose a new multi-edge feature fusion algorithm which is effective in solving this problem. Firstly, according to the characteristics of sheet-fed printing system, a new printing image database is established; compared with the existing databases, it has larger translation, deformation, and uneven illumination variation. These interferences make defect detection become more challenging. Then, SIFT feature is employed to register the database. In order to reduce the number of false detections which are caused by the position, deformation, and brightness deviation between the detected image and reference image, multi-edge feature fusion algorithm is proposed to overcome the effects of these disturbances. Lastly, the experimental results of mAP (92.65%) and recall (96.29%) verify the effectiveness of the proposed method which can effectively detect defects in low-quality printing database. The proposed research results can improve the adaptability of visual inspection system on a variety of different printing platforms. It is better to control the printing process and further reduce the number of operators.

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“…The entire image is converted to grayscale because the marker colors do not carry important information (figure 3). A median filter is carried out to get rid of random noise [12]. This reduces the number of false loops in the following steps.…”

Section: Image Preparation For Processingmentioning

confidence: 99%

Algorithm for detecting spherulite marker in human blood serum facies

Malenova

Трубникова

Yashina³

et al. 2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

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One of the effective methods of early medical diagnosis is the method of wedge dehydration. It is based on the analysis of facies images. Facia is a thin film of dried human biological fluids. The presence of special structures (markers) indicates various pathologies of the organism at their earliest stages. In this article, the algorithm for detecting spherulite marker on microscopic images of human serum facies is presented. The presence of spherulites on facies is the norm. However, the atypical form of spherulite is a marker of precancerous diseases: uterine fibroids, endometrial hyperplastic processes and the mammary gland. Due to the visual analysis of the marker, its characteristic features were identified. Then algorithmic detection methods for these features were developed. The decision on the probable presence of a marker was made if there was a combination of features of this marker. As a result of the application of the developed algorithm, most images of atypical spherulites were identified.

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Two methods of adaptive median filtering of impulse noise in images

Cited by 18 publications

References 0 publications

Image Noise Removal Method Based on Improved Nonlocal Mean Algorithm

Image Noise Removal Method Based on Improved Nonlocal Mean Algorithm

Industrial Printing Image Defect Detection Using Multi-Edge Feature Fusion Algorithm

Algorithm for detecting spherulite marker in human blood serum facies

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