Improved Threshold Denoising Method Based on Wavelet Transform

Cui, Huimin; Zhao, Ran; Hou, Yanli

doi:10.1016/j.phpro.2012.05.222

Cited by 70 publications

(26 citation statements)

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“…At the next decomposition level, the Approximation component (A 1 ) is further divided into another set of A 2 , HD 2 , VD 2 , and DD 2 as shown in Fig. 2 (4) and (5) respectively [17], [19], [20], [21], [22], [25]. σ, t and λ are defined as the noise level, universal threshold and modified universal threshold respectively and are given by Eqns.…”

Section: De-noising In Wavelet Domainmentioning

confidence: 99%

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Experiments on the Capability of Wavelet Domain Noise Suppression: Comparison with Spatial Domain Noise Suppression

Rasak¹

2019

IJCA

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Image noise can occur during image acquisition, transmission or storage. Noises in images are caused by many factors such as atmospheric or intermediate media in-homogeneity and relative motion between the object and the camera. An application of wavelet transform is noise suppression in signals; image signals inclusive. The wavelet transform of a noisy color image has 2D wavelet coefficients for each of the red, green and blue channels. The 2D wavelet coefficients are grouped into Approximation part and Details part. The coefficients in the Details part with values less than a threshold value represent the noise content. For the purpose of noise suppression, Threshold Shrinkage Functions are used to modify the coefficients in the Details part. Experiments on image noise suppression capability of Hard Threshold Shrinkage Function (HTSF), Soft Threshold Shrinkage Function (STSF), Hyperbola Threshold Shrinkage Function (HYTSF), Garrote Threshold Shrinkage Function (GTSF) and Modified Garrote Threshold Shrinkage Function (MGTSF) are presented. Six types of noise are considered. Noise suppression in the wavelet domain by the five wavelet threshold shrinkage functions is compared with noise suppression in the spatial domain by the Gaussian Filter (G), Median Filter (MDN), Alpha-Trimmed Mean [4 Pixels Excluded] Filter (AT4P), Mean Filter (MEAN) and Texture Synthesis Adaptive Median Filter (TSBAMF). Optimum threshold value is obtained with the logarithm in the universal threshold equation taken to base 10. MGTSF and GTSF are found to be the best wavelet de-noising schemes for the suppression of Poisson noise and Speckle noise respectively. HYTSF is found to be the best wavelet de-noising scheme for both the Localvar noise and the Gaussian noise. Wavelet denoising is recommended to be limited to first decomposition level as filtered images at higher decomposition levels are marred with blurring and undesirable edges. Wavelet denoising is found not suitable for the suppression of Random Valued Impulse noise and Salt & Pepper noise; spatial domain de-noising is highly effective for both noise types. Suppression of Localvar noise, Poisson noise, Gaussian noise, and Speckle noise is found to yield good results in the wavelet domain but yield better results in the spatial domain.

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Section: De-noising In Wavelet Domainmentioning

confidence: 99%

“…σ, t and λ are defined as the noise level, universal threshold and modified universal threshold respectively and are given by Eqns. (6), (7) and (8) respectively [17], [19], [20], [21], [22], [25].…”

Section: De-noising In Wavelet Domainmentioning

confidence: 99%

Experiments on the Capability of Wavelet Domain Noise Suppression: Comparison with Spatial Domain Noise Suppression

Rasak¹

2019

IJCA

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“…On the other hand, if the threshold is lower, we cannot suppress enough noise such that bad performance will be obtained. So many methods are proposed to select the threshold values in the last decades such as Sqtwolog, Maximin, Stein and Heursure [19,20,21,22,23,24]. In general, most of the methods proposed are based on the global threshold selection.…”

Section: Time-frequency Wavelet Denoisingmentioning

confidence: 99%

Wavelet Autocorrelation and Denoising of Spread Spectrum Signal for Narrowband Wireless Indoor Positioning

Wenyang¹,

Luo²

2017

dtetr

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Abstract. Channel noise is a key factor that affects the measurement accuracy of narrowband wireless indoor positioning. To reduce noise effects, it is often required to effectively filter the received spread spectrum signals. However, current filtering methods in the time or frequency domain can only filter high frequency band noise, making the measurement accuracy low. In this paper, a low frequency band noise reduction algorithm based on the threshold segmentation of the rectangular window is proposed. The method is based on thresholding on time-frequency wavelet domain by variance analysis and signal despreading is achieved by wavelet autocorrelation at low frequency band after denoising with reference to locally generated spread spectrum signal without added noise at the same subband. Simulation results show that compared with other filtering methods, the proposed method by wavelet autocorrelation and denoising can eliminate the noise effects at the receiver, leading to improving the positioning accuracy significantly.

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“…In recent years, with the development of threshold wavelet mathematical theory, structural damage identification techniques based on point cloud data have been widely discussed. To a certain extent, the stable and efficient wavelet threshold de-noising algorithm can suppress the random noise in the process of point cloud formation [12][13][14], recognizing and preserving the original low frequency signal and the non noise high frequency signal well. In this way, a new way is provided for the damage identification of structural surface crack based on point cloud data.…”

Section: Introductionmentioning

confidence: 99%

Study on Damage Identification of Beam Bridge Based on Characteristic Curvature and Improved Wavelet Threshold De-Noising Algorithm

Chu¹,

Zhou²,

Deng³

et al. 2017

AMA_B

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With the point cloud data of box girder obtained by the theory of structure from motion (SFM) algorithm chosen as the research background, a damage identification method based on characteristic curvature and improved wavelet threshold de-noising algorithm is presented. Firstly, the static load test is carried out for the full-scale box girder model, and after the cracking damage, the discrete point cloud data on the surface of the box girder are obtained through the SFM theory. According to the basic hypothesis, deformation is mainly caused by the bending moment, and micro damage has no effect on stress redistribution. Therefore, a conclusion can be made that the curvature is sensitive to the structural damage. Then, a characteristic curvature damage identification method, which is based on point cloud chord length, is used to solve the characteristic curvature of the specified section of the box girder. It is found that there are a large number of point cloud noise signals in the characteristic curvature, and on this basis, a new wavelet de-noising method established upon the threshold function is proposed. Finally, the damage index revealed from the characteristic curvature after de-noising in the specified section is compared with the actual damage location of the box girder. The results show that by combining the characteristic curvature algorithm based on the chord length of scattered point cloud with the improved wavelet threshold de-noising function, the structural surface crack damage based on spatial point cloud data can be identified. Especially, in consideration of noise interference, the improved wavelet threshold de-noising function proposed in this paper can effectively suppress the high frequency noise signal in the characteristic curvature and preserve 506 the low-frequency signal of damage in the damage area. Thus, the accuracy and sensitivity of the damage identification method based on the characteristic curvature are guaranteed. This method has the potential to be applied to structural health monitoring, since it can provide a new technical method for the early warning of the beam structure bridge.

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Improved Threshold Denoising Method Based on Wavelet Transform

Cited by 70 publications

References 0 publications

Experiments on the Capability of Wavelet Domain Noise Suppression: Comparison with Spatial Domain Noise Suppression

Experiments on the Capability of Wavelet Domain Noise Suppression: Comparison with Spatial Domain Noise Suppression

Wavelet Autocorrelation and Denoising of Spread Spectrum Signal for Narrowband Wireless Indoor Positioning

Study on Damage Identification of Beam Bridge Based on Characteristic Curvature and Improved Wavelet Threshold De-Noising Algorithm

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