Undecimated Wavelet Transforms for Image De-noising

Gyaourova, Aglika; Kamath, Chandrika; Fodor, Imola K.

doi:10.2172/15002085

Cited by 46 publications

(26 citation statements)

References 13 publications

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“…The size of the coefficients array does not diminish from level to level [36]. This decomposition is further iterated up to level 4.…”

Section: Undecimated Discrete Wavelet Transform Methodsmentioning

confidence: 99%

“…Thus its denoising performance can change drastically if the starting position of the signal is shifted. In order to achieve the shift invariance and to get more complete characteristic of the analyzed signal, the undecimated DWT (UDWT) has been proposed [36][37][38][39]. Mencattini et al developed methods for the reduction of noise in mammographic images, based on UDWT using subband noise variance computation both for homoscedastic and heteroscedastic noise [40,41].…”

Section: Introductionmentioning

confidence: 99%

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A Modified Undecimated Discrete Wavelet Transform Based Approach to Mammographic Image Denoising

et al. 2012

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In this work, the authors present an effective denoising method to attempt reducing the noise in mammographic images. The method is based on using hierarchical correlation of the coefficients of discrete stationary wavelet transforms. The features of the proposed technique include iterative use of undecimated multi-directional wavelet transforms at adjacent scales. To validate the proposed method, computer simulations were conducted, followed by its applications to clinical mammograms. Mutual information originating from information theory was used as an evaluation measure for selection of an optimal wavelet basis function. We examined the performance of the proposed method by comparing it with the conventional undecimated discrete wavelet transform (UDWT) method in terms of processing time-consuming and image quality. Our results showed that with the use of the proposed method the computation time can be reduced to approximately 1/10 of the conventional UDWT method consumed. The results of visual assessment indicated that the images processed with the proposed UDWT method showed statistically significant superior image quality over those processed with the conventional UDWT method. Our research results demonstrate the superiority and effectiveness of the proposed approach.

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“…The size of the coefficients array does not diminish from level to level [36]. This decomposition is further iterated up to level 4.…”

Section: Undecimated Discrete Wavelet Transform Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Modified Undecimated Discrete Wavelet Transform Based Approach to Mammographic Image Denoising

et al. 2012

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“…Many of the wavelet based denoising algorithms use DWT (Discrete Wavelet Transform) in the decomposition stage which suffers from shift variance and boundary discontinuity [24], [29][30][31]. This problem is eliminated to a large extent using Undecimated Wavelet Transform [34][35][36][37][38]. The problems of resolution were satisfactorily addressed by multi-resolution and multi-scale capabilities of Wavelet transform [5], [6] to large extent.…”

Section: Y (X Y) =I (X Y) +N(x Y)mentioning

confidence: 99%

Denoising of Digital Images using Consolidation of Edges and Separable Wavelet Transform

Neole¹,

Lad²,

Bhurchandi³

2017

IJCA

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Denoising is still a challenging area of research due to its commercial and technical applications. We present a novel approach to image denoising using edge profile detection and edge preservation in spatial domain in presence of zero mean additive Gaussian noise. A Noisy image is initially preprocessed using the proposed local edge profile detection and subsequent edge preserving filtering in spatial domain followed further by the modified threshold bivariate shrinkage algorithm. The proposed technique does not require any estimate of standard deviation of noise (σ) present in the image. Performance of the proposed algorithm is presented in terms of PSNR and SSIM on a variety of test images containing a wide range of σ starting from 15 to 100. The performance of the proposed algorithm is better than NL means and Bivariate Shrinkage while it's comparable with BM3D.

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“…Earlier for reduction of speckle researchers use discrete wavelet transformation [1][4][5] [7][8] [9].But drawback of DWT is that it is translation variant [10]. Some important coefficients can be lost during transformation from original signal to sub bands.…”

Section: Introductionmentioning

confidence: 99%

Despeckling of SAR Images using Wavelet based Iterative Brute Force Thresholding with Median Filter and Savitzky-Golay Filter

Gir¹,

Jain²,

Rai³

2015

IJCA

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SAR images are corrupted by speckle noise which is based on multiplicative noise or Reyleigh noise. The speckle degrades the quality of image and makes interpretations, analysis and classifications of SAR images harder. Therefore some speckle mitigation is necessary prior to the processing of SAR images. In this paper a new method is proposed for despeckling of SAR images in which Savitzky-Golay filter and median filter are used for denoising of the synthetic aperature radar (SAR) image. After obtaining filtered image they are decomposed by the use of undecimated wavelet transform. The speckled input image is also decomposed using undecimated wavelet transform. Then image segmentation is done by the use of brute force thresholding wavelet based algorithm in which each pixel of the entire decomposed image is compared and the maximum value of threshold image pixel is replaced in every iteration of image processing. Lastly enhanced directional smoothing of the image is done to obtain a despeckled image General TermsUndecimated Wavelet Transform, SAR, Savitzky-Golay filter, median filter, direction dependent mask, Directional Smoothing.

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Undecimated Wavelet Transforms for Image De-noising

Cited by 46 publications

References 13 publications

A Modified Undecimated Discrete Wavelet Transform Based Approach to Mammographic Image Denoising

A Modified Undecimated Discrete Wavelet Transform Based Approach to Mammographic Image Denoising

Denoising of Digital Images using Consolidation of Edges and Separable Wavelet Transform

Despeckling of SAR Images using Wavelet based Iterative Brute Force Thresholding with Median Filter and Savitzky-Golay Filter

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