Image denoising by adaptive lifting schemes

Abid, Manel; Cagnazzo, Marco; Pesquet‐Popescu, Béatrice

doi:10.1109/euvip.2010.5699143

Cited by 2 publications

(4 citation statements)

References 17 publications

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“…From (9), this approach is simply to calculate the threshold of the first level, and then employ the recursive method to calculate the other level wavelet threshold. α ， β can be obtained by offline experimental analysis or experience.…”

Section: Recursive Threshold Value Computing Methodmentioning

confidence: 99%

“…Where, ( ) sign ⋅ is Signum function, i λ is the ith level threshold value obtained from (9). Hyperbolic semisoft shrinkage function overcomes the shortcomings of the hardthresholding function and the soft-thresholding function.…”

Section: Hyperbola Thresholding Functionmentioning

confidence: 99%

“…Such scheme can adapt itself well to the analyzed signal, which allows keeping important information for denoising applications. Although Literature [8] and [9] made a good noise reduction effect by using lifting wavelet transform, this wavelet denoising method is usually employed in offline mode, thus its application scope is limited. In order to satisfy the requirements of real-time signal denoising, Xia [10] proposed an online wavelet denoising approach.…”

Section: Introductionmentioning

confidence: 99%

“…M. Abid [9] proposed an image denoising method by using the adaptive lifting scheme, this method estimates the noise energies in the subband and uses lifting wavelet to perform image denoising. Such scheme can adapt itself well to the analyzed signal, which allows keeping important information for denoising applications.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improved Real-time Denoising Method Based on Lifting Wavelet Transform

Liu

Mao

2014

Measurement Science Review

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Signal denoising can not only enhance the signal to noise ratio (SNR) but also reduce the effect of noise. In order to satisfy the requirements of real-time signal denoising, an improved semisoft shrinkage real-time denoising method based on lifting wavelet transform was proposed. The moving data window technology realizes the real-time wavelet denoising, which employs wavelet transform based on lifting scheme to reduce computational complexity. Also hyperbolic threshold function and recursive threshold computing can ensure the dynamic characteristics of the system, in addition, it can improve the real-time calculating efficiency as well. The simulation results show that the semisoft shrinkage real-time denoising method has quite a good performance in comparison to the traditional methods, namely soft-thresholding and hard-thresholding. Therefore, this method can solve more practical engineering problems.

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Section: Recursive Threshold Value Computing Methodmentioning

confidence: 99%

Section: Hyperbola Thresholding Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved Real-time Denoising Method Based on Lifting Wavelet Transform

Liu

Mao

2014

Measurement Science Review

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Translation Invariant Directional Framelet Transform Combined With Gabor Filters for Image Denoising

Shi

Yang

Guo

2014

IEEE Trans. on Image Process.

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This paper is devoted to the study of a directional lifting transform for wavelet frames. A nonsubsampled lifting structure is developed to maintain the translation invariance as it is an important property in image denoising. Then, the directionality of the lifting-based tight frame is explicitly discussed, followed by a specific translation invariant directional framelet transform (TIDFT). The TIDFT has two framelets ψ1, ψ2 with vanishing moments of order two and one respectively, which are able to detect singularities in a given direction set. It provides an efficient and sparse representation for images containing rich textures along with properties of fast implementation and perfect reconstruction. In addition, an adaptive block-wise orientation estimation method based on Gabor filters is presented instead of the conventional minimization of residuals. Furthermore, the TIDFT is utilized to exploit the capability of image denoising, incorporating the MAP estimator for multivariate exponential distribution. Consequently, the TIDFT is able to eliminate the noise effectively while preserving the textures simultaneously. Experimental results show that the TIDFT outperforms some other frame-based denoising methods, such as contourlet and shearlet, and is competitive to the state-of-the-art denoising approaches.

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Image denoising by adaptive lifting schemes

Cited by 2 publications

References 17 publications

Improved Real-time Denoising Method Based on Lifting Wavelet Transform

Improved Real-time Denoising Method Based on Lifting Wavelet Transform

Translation Invariant Directional Framelet Transform Combined With Gabor Filters for Image Denoising

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