Image denoising with an optimal threshold and neighbouring window

Zhou, Dengwen; Cheng, Wengang

doi:10.1016/j.patrec.2008.04.014

Cited by 106 publications

(80 citation statements)

References 11 publications

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“…In sub band thresholding, the threshold and neighboring window size keep unchanged in all sub bands. Neigh Shrink Sure [14] is an improvement over Neigh Shrink [12], which has disadvantage of using a non-optimal universal threshold value and the same neighboring window size in all wavelet sub bands. Neigh Shrink Sure.…”

Section: F Neigh Shrink Surementioning

confidence: 99%

“…Neigh Shrink Sure. It can determine an optimal threshold and neighboring window size for every sub band by the Stein's unbiased risk estimate (SURE) [14]. They combine the unknown noiseless coefficients from sub bands into the corresponding 1-D vector.…”

Section: F Neigh Shrink Surementioning

confidence: 99%

“…risk) can be estimated in an unbiased way. They estimated optimal threshold value and sub band level using following [14]…”

Section: F Neigh Shrink Surementioning

confidence: 99%

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A Comparative Study of Wavelet Thresholding for Image Denoising

Dixit¹,

Sharma²

2014

IJIGSP

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Abstract-Image denoising using wavelet transform has been successful as wavelet transform generates a large number of small coefficients and a small number of large coefficients. Basic denoising algorithm that using the wavelet transform consists of three steps -first computing the wavelet transform of the noisy image, thresholding is performed on the detail coefficients in order to remove noise and finally inverse wavelet transform of the modified coefficients is taken. This paper reviews the state of art methods of image denoising using wavelet thresholding. An Experimental analysis of wavelet based methods Visu Shrink, Sure Shrink, Bayes Shrink, Prob Shrink, Block Shrink and Neigh Shrink Sure is performed. These wavelet based methods are also compared with spatial domain methods like median filter and wiener filter. Results are evaluated on the basis of Peak Signal to Noise Ratio and visual quality of images. In the experiment, wavelet based methods perform better than spatial domain methods. In wavelet domain, recent methods like prob shrink, block shrink and neigh shrink sure performed better as compared to other wavelet based methods.

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Section: F Neigh Shrink Surementioning

confidence: 99%

Section: F Neigh Shrink Surementioning

confidence: 99%

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A Comparative Study of Wavelet Thresholding for Image Denoising

Dixit¹,

Sharma²

2014

IJIGSP

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“…VisuShrink (Donoho, 1995), SUREShrink (Donoho and Johnstone, 1995) and BayesShrink (Chang et al, 2000) are the different methods proposed for the selection of threshold value. Chen et al (2004) proposed a wavelet thresholding scheme based on wavelet coefficients within a neighborhood and its improved version NeighShrinkSURE was proposed by Dengwen and Wengang (2008). In SmoothShrink (Mastriani and Giraldez, 2005) a Directional Smoothing (DS) function is used to reduce the speckle noise in Synthetic Aperture Radar (SAR) images.…”

Section: Introductionmentioning

confidence: 99%

Speckle Reduction in Ultrasound Images Using Neighshrink and Bilateral Filtering

Vanithamani¹

2014

Journal of Computer Science

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Speckle is a random multiplicative noise which obscures the perception and extraction of fine details in ultrasound images and speckle reduction is necessary to improve the visual quality of ultrasound images for better diagnosis. This study aims at introducing an algorithm by hybridizing bilateral filter with NeighShrink. The bilateral filter is applied before decomposition and after reconstruction of the image using discrete wavelet transform to improve the denoising efficiency and preserve the edge features effectively. The wavelet thresholding scheme NeighShrink is used for thresholding of wavelet coefficients. The algorithm is tested with synthetically speckled and real ultrasound images. Quality evaluation metrics such as Peak Signal to Noise Ratio (PSNR), Edge Preservation Index (EPI) and Correlation Coefficient (CoC) are used to assess the performance of the proposed method. Experimental results show that the proposed scheme improves the visual quality of ultrasound images by suppressing the speckle noise while retaining edges.

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“…White Gaussian noise can be caused by poor image acquisition or by transferring the image data in noisy communication channel. Most denoising algorithms use images artificially distorted with well defined white Gaussian noise to achieve objective test results [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Image Denoising based on Spatial/Wavelet Filter using Hybrid Thresholding Function

Hussain¹,

Gorashi²

2012

IJCA

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In this paper a hybrid denoising algorithm which combines spatial domain bilateral filter and hybrid thresholding function in the wavelet domain is proposed. The wavelet transform is used to decompose the noisy image into its different subbands namely LL, LH, HL, and HH. A two stage spatial bilateral filter is applied. The first stage is applied on the noisy image before wavelet decomposition. This stage will be called a preprocessing stage. The second stage spatial bilateral filtering is applied on the low frequency subband of the decomposed noisy image namely subband LL. This stage will tend to cancel or at least attenuate any residual low frequency noise components. The intermediate stage deal with high frequency noise components by thresholding detail subbands LH, HL, and HH using hybrid thresholding function. The experimental results show that the performance of the proposed denoising algorithm is superior to that of the conventional denoising approach. General TermsImage Denoising. Wavelet Transform.

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Image denoising with an optimal threshold and neighbouring window

Cited by 106 publications

References 11 publications

A Comparative Study of Wavelet Thresholding for Image Denoising

A Comparative Study of Wavelet Thresholding for Image Denoising

Speckle Reduction in Ultrasound Images Using Neighshrink and Bilateral Filtering

Image Denoising based on Spatial/Wavelet Filter using Hybrid Thresholding Function

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