A noise reduction approach based on Stein′s unbiased risk estimate

Guo, Qiang; Zhang, Caiming

doi:10.2306/scienceasia1513-1874.2012.38.207

Cited by 14 publications

(5 citation statements)

References 16 publications

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“…Although the ZLE does not offer the on-line processing property of ACF, the ZLE can process the data in real-time substantially because of its low computational complexity O ( N ) where N means the length of the input signal. The computational complexity of wavelet de-noising is O ( N log N ) [ 33 ] and that of others (CFSA, ACF, and ZLE) is O ( N ).…”

Section: Discussionmentioning

confidence: 99%

Blockwise PPG Enhancement Based on Time-Variant Zero-Phase Harmonic Notch Filtering

Park

Shin

Lee

2017

Sensors

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So far, many approaches have been developed for motion artifact (MA) reduction from photoplethysmogram (PPG). Specifically, single-input MA reduction methods are useful to apply wearable and mobile healthcare systems because of their low hardware costs and simplicity. However, most of them are insufficiently developed to be used in real-world situations, and they suffer from a phase distortion problem. In this study, we propose a novel single-input MA reduction algorithm based on time-variant forward-backward harmonic notch filtering. To verify the proposed method, we collected real PPG data corrupted by MA and compared it with existing single-input MA reduction methods. In conclusion, the proposed zero-phase line enhancer (ZLE) was found to be superior for MA reduction and exhibited zero phase response.

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Section: Discussionmentioning

confidence: 99%

Blockwise PPG Enhancement Based on Time-Variant Zero-Phase Harmonic Notch Filtering

Park

Shin

Lee

2017

Sensors

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“…Alternatively, we may use Stein's unbiased risk estimator, which is widely used as an efficient parameter selection method [17][18][19] , to derive the adaptive parameter. In addition, we could reduce the computational burden by optimizing the whole process further, particularly the available speed-up methods for computing nonlocal weights.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Image interpolation based on nonlocal self-similarity

Guo¹,

Zhang²,

Liu³

et al. 2014

ScienceAsia

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This paper presents an efficient method for the resolution enhancement of low resolution (LR) images. Inspired by the great success of nonlocal filtering, the proposed method interpolates a missing pixel by a nonlocal estimation which takes into account the self-similarity of images and the geometric duality between LR images and high resolution (HR) images. This method firstly exploits the SAI method to generate an initial HR image of the given LR image. Then the initial HR image is further improved by a nonlocal estimation that uses the given LR image as a guiding image. Pixel correction is applied to reduce the distortion of the HR estimate. Experimental results demonstrate that the proposed method provides competitive performance.

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“…This methodology adopts that the image is commonly signified meagerly over some illustration source similar wavelets also its resulting methods and also the noise feasts consistently throughout the image thus the obligatory image info focusses only at some examples. Thus, noise is distinguished effectively through varied shrinkage techniques, like Sureshrink [20], [21], Multi Shrink [22], Bayes Shrink [23], Bi Shrink [24] and Preshrink [25].Though these ways succeed an efficient image denoising, the fixed wavelet convert fails in distributing through line also point individualities toward arrange for an adaptive illustration on behalf of the noisy image comprising the line in addition to point originalities.. The other form of the wavelet, SWT overcome this problem and having interpolation of coefficients in filter and by DWT technique the shift invariance is achieved by removing some samples.…”

Section: B Transform Domain Approachesmentioning

confidence: 99%

An Image Denoising Framework based on Patch Grouping in Complex Wavelet Domain

Chinnarao¹

2019

IJATCSE

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In the Multimedia communication, more number of digital images are transferred from one end to another. During the channel some noise can interfere and degrade the image. Hence the removal of noise is given much attention towards research community. This method addressed a denoising technique with the support of DT-CWT also patch combination. This approach gives the direction of decomposition under DT-CWT. Patch group is enabled to protect the corner frequencies effectively, through the known spreading of noise over a image. Euclidian distance is utilized to formulate the patches in to clusters, further it is processed for denoising through adaptive wavelet thresholding. The denoised image could be received by imposing inverse transformation. The simulation results indicate that our algorithm withstands for various noises, also gives the better PSNR and SSIM. The simulation result exposed the exceptional presentation of the projected method each within the maintenance of essential features also quality improvisation.

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A noise reduction approach based on Stein′s unbiased risk estimate

Cited by 14 publications

References 16 publications

Blockwise PPG Enhancement Based on Time-Variant Zero-Phase Harmonic Notch Filtering

Blockwise PPG Enhancement Based on Time-Variant Zero-Phase Harmonic Notch Filtering

Image interpolation based on nonlocal self-similarity

An Image Denoising Framework based on Patch Grouping in Complex Wavelet Domain

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