Multiscale adaptive regularisation Savitzky–Golay method for speckle noise reduction in ultrasound images

Sa-ing, Vera; Vorasayan, Pongpat; Suwanwela, Nijasri C.; Auethavekiat, Supatana; Chinrungrueng, C.

doi:10.1049/iet-ipr.2017.0391

Cited by 23 publications

(10 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These functions are a heuristic generalization of the weights used in weighted least-squares regression [21]. Regularization methods that penalize derivatives are another way of suppressing the high-frequency gain [1], [22], [23]. These modifications do have the desired effect; however, trial-and-error tuning may be required for the desired bandwidth as they lack frequency-domain design parameters.…”

Section: B Standard Sg Filtersmentioning

confidence: 99%

“…dilute, distant, or covert), and the environment cluttered, additional processing/filtering is necessary to ensure satisfactory performance. Digital linear-phase finite-impulse-response (FIR) realizations of polynomial regression filters, sometimes known as Savitzky-Golay (SG) filters (named after the physical chemists who first applied them to the smoothing of spectra), are commonly used in such problems; for instance in: image-processing [1], [2], power-engineering [3], [4], and bio-medical applications [5], [6], [7], [8], [9]. SG-filters are popular and have a long history because they have: a low computational complexity, for fast realization in online systems; and a simple mathematical foundation, for the intuitive interpretation of their operation and output.…”

Section: Introductionmentioning

confidence: 99%

“…SG-filters are usually used to reduce noise in low-pass smoothers or band-pass differentiators [1]- [9]. Due to their ability to perfectly match the desired frequency-response at the dc limit, they are particularly useful for analysing very low-frequency phenomena in oversampled time series.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving the frequency response of Savitzky-Golay filters via colored-noise models

Kennedy

2020

Digital Signal Processing

View full text Add to dashboard Cite

Savitzky-Golay (SG) filters are finite impulse response (FIR) realizations of least-squares polynomial regression and they are widely used for filtering (e.g. smoothing, interpolating, predicting, differentiating) and processing (e.g. detecting and classifying) non-stationary signals in non-Gaussian noise. For such inputs, the Wiener filter is biased and the Kalman filter is sub-optimal. Sequentially-correlated (i.e. 'colored') noise models are an integral part of the Wiener filter and an optional addition to the Kalman filter; however, their use in SG-filters has been overlooked in recent times. It is shown here that colored (wide-band and narrow-band) noise models are readily incorporated into a standard SG-filter and that this also addresses the well-known deficiency of their poor frequency-selectivity/configurability. A wide-band noise model sets the main-lobe/side-lobe width/height and provides physical justification for band-limited design procedures described elsewhere. The proposed narrow-band noise model, with arbitrarily placed side-lobe nulls, has the potential to outperform other SG filters when sinusoidal interferers of known frequency are present. The utility of these 'whitened' SGfilters is illustrated in a hypothetical pulse/peak-detection application using a test statistic that is shaped by the noise model.

show abstract

Section: B Standard Sg Filtersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving the frequency response of Savitzky-Golay filters via colored-noise models

Kennedy

2020

Digital Signal Processing

View full text Add to dashboard Cite

show abstract

“…The competition methods compared in this study include BM3D [48], EPLL [49] , NCSR [50], WNNM [51], Multiscale [50], DnCNN [17] and the proposed SlimRGBD. In particular, in order to reveal the limitations of the method based on discriminative learning in dealing with blind denoising problems, a blind model of DnResNet available for Gaussian image denoising is employed in the evaluation.…”

Section: B Experimentsmentioning

confidence: 99%

SlimRGBD: A Geographic Information Photography Noise Reduction System for Aerial Remote Sensing

Wu¹,

Ju²,

Wu³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In the past ten years, civil drone technology has developed rapidly, and UAV (Unmanned Aerial Vehicle) has been widely used in various industries. Especially in the field of aerial remote sensing, the emergence of UAV technology has enabled the geographical information of remote areas that are not concerned to be quickly presented. However, UAV aerial photography is greatly affected by the weather. Pictures that use aerial drones for aerial photography in rainy weather will appear noise. In this paper, how to eliminate the noise of aerial image is to be talked, the multi-channel pruning technology is used to pruning the RnResNet network. Based on this, a new anti-convergence-convolution neural network noise reduction system for the operation of UAV airborne embedded equipment is proposed. The system is used to eliminate noise in the aerial image. This type of noise reducer has got rid of the current situation that the neural network noise reducer consumes too much power and is inefficient, and has certain advantages. INDEX TERMS SlimRGBD, ResNet, generative adversarial networks, image noise reduction, UAV, channel pruning, sparse training.

show abstract

“…Traditional filtering methods include Kalman filtering, Savitzky-Golay smoothing filtering, wavelet analysis and other filtering methods, which are all based on linear analysis for data processing [20][21][22]. Nevertheless, in practices, in addition to periodic noise, the trajectory data also contain a large number of accidental noise so they are not suitable for trajectory data processing [23].…”

Section: Introductionmentioning

confidence: 99%

Vehicle Trajectory Optimization Based on Limiting Average Algorithm

Wang

Liu

2021

IEEE Access

View full text Add to dashboard Cite

Multiscale adaptive regularisation Savitzky–Golay method for speckle noise reduction in ultrasound images

Cited by 23 publications

References 42 publications

Improving the frequency response of Savitzky-Golay filters via colored-noise models

Improving the frequency response of Savitzky-Golay filters via colored-noise models

SlimRGBD: A Geographic Information Photography Noise Reduction System for Aerial Remote Sensing

Vehicle Trajectory Optimization Based on Limiting Average Algorithm

Contact Info

Product

Resources

About