An image denoising algorithm based on adaptive clustering and singular value decomposition

Li, Ping; Wang, Hua; Li, Xuemei; Zhang, Caiming

doi:10.1049/ipr2.12017

Cited by 17 publications

(10 citation statements)

References 47 publications

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“…Based on Wavelet Adaptive reshold. Firstly, according to the wavelet decomposition characteristics of multilevel images, a better threshold suitable for denoising multilevel images with different coefficients is determined to achieve smooth denoising effect [6]. e specific process is as follows.…”

Section: Multilevel Image Denoisingmentioning

confidence: 99%

Multilevel Image Edge Detection Algorithm Based on Visual Perception

2022

Security and Communication Networks

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Multilevel image edge repair results directly affect the follow-up image quality evaluation and recognition. Current edge detection algorithms have the problem of unclear edge detection. In order to detect more accurate edge contour information, a multilevel image edge detection algorithm based on visual perception is proposed. Firstly, the digital image is processed by double filtering and fuzzy threshold segmentation; Through the analysis of the contour features of the moving image, the threshold of the moving image features is set, and the latest membership function is obtained to complete the multithreshold optimization. Adaptive smoothing is used to process the contour of the object in the moving image, and the geometric center values of the two adjacent contour points within the contour range are calculated. According to the calculation results, the curvature angle is further calculated, and the curvature symbol is obtained. According to the curvature symbol, the contour features of the moving image are detected. The experimental results show that the proposed algorithm can effectively and accurately detect the edge contour of the image and shorten the reconstruction time, and the detection image resolution is high.

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Section: Multilevel Image Denoisingmentioning

confidence: 99%

Multilevel Image Edge Detection Algorithm Based on Visual Perception

2022

Security and Communication Networks

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“…Tripathi et al used singular value decomposition to analyze images [21]. Li et al denoised images using the images' singular value [22]. The singular value of the color channel reflects the image's features, such as contrast.…”

Section: Proposed Methodsmentioning

confidence: 99%

Efficient Sandstorm Image Color Correction Using Rank-Based Singular Value Recombination

Lee¹

2022

Symmetry

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Sandstorm images have a color cast that is reddish or yellowish due to the attenuation of the color channel. When light propagates through sand particles, it is scattered. Additionally, if some sand particles have a certain color, the obtained image in this circumstance experiences a color shift because some color channels are attenuated. Therefore, sandstorm images have a symmetrically distributed color cast throughout the image. There have been many studies aiming to enhance sandstorm images. In many studies, to enhance color-casted images, researchers have used the various methods as with gamma correction. However, artificial color shift occurs in an enhanced image because these methods do not reflect the image’s adaptive feature. This paper proposes a sandstorm image enhancement method using singular value recombination based on rank. The singular value of an image reflects the image’s characteristics adaptively, and improved images using the singular values have no artificial color because the balanced image eliminates the degraded color cast. Because the balanced image has hazy or dusty features, the singular value ratio can be used to enhance the image. The enhanced images produced using the proposed method are superior compared to state-of-the-art methods, objectively and subjectively.

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“…To reduce the dimensionality of clustering data, traditional dimensionality reduction techniques are introduced into the representation of clustering data, including Principal Component Analysis (PCA) [28,29], t-Distributed Stochastic Neighbor Embedding (t-SNE) [30,31], and Singular Value Decomposition (SVD) [32,33], among others. These dimensionality reduction techniques are characterized by their simplicity, ease of implementation, and suitability for linear datasets.…”

Section: Related Workmentioning

confidence: 99%

Large-Scale Stream K-means Based on Product-Quantized Codes

Hang,

Yin,

et al. 2024

Preprint

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In recent years, using clustering technology to process large-scale data streams is a research hotspot in the field of data mining. However, for the processing of large-scale data streams, most existing methods suffer from slow speed, insufficient memory, and lack of detection and response mechanisms for concept drift. In this paper, a Large-Scale Stream K-measn based on product-quantized codes (LS2K-means) is proposed. By first introducing product quantization code into the framework of incremental clustering methods, memory space consumption is reduced through the dimensionality reduction of data. Additionally, a new similarity measurement method is introduced, greatly improving the efficiency of distance calculation. A concept drift detection and response mechanism is constructed. By comparing the consistency of clustering results, concept drift can be quickly detected, and a backtracking mechanism is utilized to respond to concept drift promptly, effectively improving the algorithm’s performance. The effectiveness of the proposed method is validated through simulations on six real datasets. The method efficiently handles concept drift and outperforms DenStream and EmCStream in terms of execution efficiency.

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An image denoising algorithm based on adaptive clustering and singular value decomposition

Cited by 17 publications

References 47 publications

Multilevel Image Edge Detection Algorithm Based on Visual Perception

Multilevel Image Edge Detection Algorithm Based on Visual Perception

Efficient Sandstorm Image Color Correction Using Rank-Based Singular Value Recombination

Large-Scale Stream K-means Based on Product-Quantized Codes

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