Non-local Means Denoising Algorithm Based on Local Binary Patterns

Kartsov, S. K.; Kupriyanov, D. Yu.; Polyakov, Yu. A.; Zykov, Aleksey

doi:10.1007/978-3-030-39177-5_12

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…To identify the most loaded sections of the crusher drive shaft, a static analysis and strength analysis were performed using Autodesk Inventor 2020 software. Strength criterion is one of the most significant operability criterion of machine parts [30][31][32][33][34][35][36][37][38][39][40].…”

Section: Analytical Calculationsmentioning

confidence: 99%

Static analysis and strength calculation of drive shaft of large-scale cone crusher

et al. 2020

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The paper analyzes the causes of failures of coarse crushers “KKD 1500-180” at JSC “Almalyk MMC”. It has been established that the most common cause of these failures is a drive shaft failure. Static analysis of the strength of the drive shaft was performed using Autodesk Inventor 2020 CAD software. A dangerous shaft section was found, and recommendations aimed at improving its operability were proposed.

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Section: Analytical Calculationsmentioning

confidence: 99%

Static analysis and strength calculation of drive shaft of large-scale cone crusher

et al. 2020

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“…These algorithms achieve image clustering by extracting local features of an image, including image clustering algorithms based on local binary codes [9] and image clustering algorithms based on local texture features of an image [10] . In recent years, with the development of deep learning techniques, more and more research work has focused on unsupervised image clustering algorithms based on deep learning.…”

Section: Introductionmentioning

confidence: 99%

End-to-end unsupervised clustering neural networks for image clustering

Li¹,

Li²,

Zhu³

2023

Preprint

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<p>In this paper, we propose a new clustering module that can be trained jointly with existing neural network layers. Specifically, we have designed a generic clustering module with a competitive update mechanism. The module consists of a Gaussian unit and a maximum pooling layer. The Gaussian unit forward propagation conforms to the joint Gaussian distribution and contains two sets of trainable parameters. It requires no tedious setup and has a plug-and-play feature. To improve the representation capability of the network, we used an auto-encoder to extract the hidden semantics of the input features and combined the clustering module with the auto-encoder to construct an end-to-end unsupervised clustering neural network. We refer to this as HGL_CAE(High-dimensional Gaussian distribution layers combined with convolutional autoencoders).The network is highly adaptable to different input feature dimensions and can cope with situations where the number of clusters cannot be determined in advance. We conducted experiments on the MNIST dataset and the Fashion_MNIST dataset with a clustering accuracy of 93.38% and 72.83% respectively. It is highly competitive with existing methods. </p>

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“…The Median Filter method has been applied by [5], focusing on reducing impulsive noise models with very high intensities. [6] proposed the Non-local Means algorithm by applying the concept of self-similarity. [7] proposed a Edge Preserving algorithm to reduce Salt and Pepper noise with parallel computing.…”

Section: Introductionmentioning

confidence: 99%

Modification of Non-local Mean Algorithm Using Parallel Calculation for Image Noise Reduction

Al Farissi,

Wondo

2023

j. nas. pendidik. teknik. inform.

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Noise in digital image processing is a noise that occurs at pixel values due to random colour intensity. Several types of noise models include Gaussian noise, speckle noise, impulse noise, and Poisson noise. Before processing image data, a noise reduction process is required. One of the noise reduction algorithms used for gaussian noise models is Non-local Mean. This algorithm performs calculations sequentially on each pixel in the search block. Due to a large number of pixels and search block area in the image, the noise reduction process using the Non-local Mean algorithm is very slow. This study proposes the concept of parallel calculations for the Non-local Mean algorithm. This concept divides the search block into three parts and performs calculations on each part simultaneously. The experimental results show that the Non-local Mean algorithm with parallel calculations can reduce noise up to 30% faster if the noise standard deviation is above 30.

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Non-local Means Denoising Algorithm Based on Local Binary Patterns

Cited by 9 publications

References 21 publications

Static analysis and strength calculation of drive shaft of large-scale cone crusher

Static analysis and strength calculation of drive shaft of large-scale cone crusher

End-to-end unsupervised clustering neural networks for image clustering

Modification of Non-local Mean Algorithm Using Parallel Calculation for Image Noise Reduction

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