Adaptive noise reduction algorithms based on statistical hypotheses tests

Lee, Jaeheon; Kim, Yeong-Hwa; Nam, Jin Hyun

doi:10.1109/tce.2008.4637634

Cited by 19 publications

(5 citation statements)

References 9 publications

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“…In image feature and noise detection problems, the variance of the sample in one direction is usually used as a statistical criterion to test the noise atom. Let This paper introduces the Bartlett test to weaken the effect of noise atoms on dictionary denoising 28 . This method is used to optimize dictionary atoms and improve the sparse representation ability of the dictionary.…”

Section: Optimizing Dictionary Atomsmentioning

confidence: 99%

LDCT image denoising algorithm based on two-dimensional variational mode decomposition and dictionary learning

HAN,

Zhang,

et al. 2024

Preprint

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Low-dose CT images result in lower image reconstruction quality due to reduced radiation dose, producing noise and artifacts that affect the accuracy of clinical medical diagnosis. To address this problem, we combined 2D variational modal decomposition and dictionary learning. We proposed a low-dose CT (LDCT) image denoising algorithm based on an improved K-SVD algorithm with image decomposition. The traditional K-SVD algorithm lacks consideration for the differences in structural information between images. To address this problem, we employ the two-dimensional variational mode decomposition (2D-VMD) method to decompose the image into distinct modal components. Through the adaptive learning of dictionaries based on the characteristics of each modal component, independent denoising processing is applied to each component, avoiding the loss of structural and detailed information in the image. In addition, we introduce the regularized orthogonal matching pursuit algorithm (ROMP) and dictionary atom optimization method to improve the sparse representation ability of the dictionary and reduce the impact of noise atoms on denoising performance. The experiments show that the proposed method outperforms other denoising methods regarding peak signal-to-noise ratio and structural similarity. The proposed method maintains the denoised image details and structural information while removing LDCT image noise and artifacts. The image quality after denoising is significantly improved and facilitates more accurate detection and analysis of lesion areas.

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Section: Optimizing Dictionary Atomsmentioning

confidence: 99%

LDCT image denoising algorithm based on two-dimensional variational mode decomposition and dictionary learning

HAN,

Zhang,

et al. 2024

Preprint

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“…The noise may be introduced to the signal at the time of transmission or due to the limitations of the devices being used for signal processing (Lee et al, 2007). (Lee et al, 2008). The waveform at the final stage is compared with the waveforms of Composite Step Signal Generator at the point of transmission (Tanaka, 2006).…”

Section: Distorted Composite Signals Waveformsmentioning

confidence: 99%

Detection of Noise in Composite Step Signal Pattern by Visualizing Signal Waveforms

Malhotra¹,

Verma

2018

European Journal of Sustainable Development Research

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The Step Composite Signals is the combination of vital informative signals that are compressed and coded to produce a predefined test image on a display device. It carries the desired sequence of information from source to destination. This information may be transmitted as digital signal, video information or data signal required as an input for the destination module. For testing of display panels, Composite Test Signals are the most important attribute of test signal transmission system. In the current research paper we present an approach for the noise detection in Composite Step Signal by analysing Composite Step Signal waveforms. The analysis of the signal waveforms reveals that the noise affected components of the signal and subsequently noise reduction process is initiated which targets noisy signal component only. Thus the quality of signal is not compromised during noise reduction process.

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“…The figure No.3 shows the waveforms of distorted Composite Signal [6]. The waveform of the signal received is compared with the waveforms observed before transmission [7].…”

Section: Distorted Composite Signals Waveformsmentioning

confidence: 99%

Noise Detection in Composite Signals by Analysing Signal Waveforms

Malhotra¹,

Singh²

2013

IJAIS

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The Composite Signals are the combination of multi utility signals, compressed signals and coded signals to carry the information from source to destination. This information may be a digital signal, video information or data signal required as an input for the destination module. Presently the Composite Signals are basic components of any signal transmission system. In this paper we present an approach to detect the noise in composite signals by analysing signal waveforms. General Terms

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Adaptive noise reduction algorithms based on statistical hypotheses tests

Cited by 19 publications

References 9 publications

LDCT image denoising algorithm based on two-dimensional variational mode decomposition and dictionary learning

LDCT image denoising algorithm based on two-dimensional variational mode decomposition and dictionary learning

Detection of Noise in Composite Step Signal Pattern by Visualizing Signal Waveforms

Noise Detection in Composite Signals by Analysing Signal Waveforms

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