Wavelet shrinkage and generalized cross validation for image denoising

Weyrich, Norman; Warhola, Gregory T.

doi:10.1109/83.650852

Cited by 92 publications

(41 citation statements)

References 13 publications

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“…BF is the bilateral filter, MBF represent the method proposed in the article [3], the third method is the proposed method. For the proposed method, we set the parameters as follow: [σ δ ， σ r ]=[4,0.23], λ=2.0 turned out to be a better choice in terms of Signal-to-Noise Ratio(PSNR), For the NSCT, we decompose the image into three levels, and set the direction respectively as follow: [2,4,8]. we choice the "maxflat"as nonsubsampled pyramids and " dmaxflat7 " as nonsubsampled directional filter banks in Matlab version 7.0.…”

Section: The Results and Discussionmentioning

confidence: 99%

Image Denoising Algorithm Based on Non-Subsampled Contourlet Transform and Bilateral Filtering

Li¹,

Yang²,

Cai³

et al. 2015

Proceedings of the International Conference on Computer Information Systems and Industrial Applications

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Abstract-In order to remove the image noise more effectively，an image denoising algorithm based on Nonsubsampled Contourlet Transform (NSCT) and bilateral filtering in the spatial domain is proposed. The noisy image is first decomposed into multi-scale and multi-directional subbands by NSCT, and direction subbands of each high-pass component is processed by the new threshold function which is obtained by the Bayes threshold that based on stratified noise estimation.. During the reconstruction, the low-pass subband constructed image is further denoisied by the bilateral filtering in the spatial domain. Experimental results demonstrate that the proposed method can improve de-noising performance.

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Section: The Results and Discussionmentioning

confidence: 99%

Image Denoising Algorithm Based on Non-Subsampled Contourlet Transform and Bilateral Filtering

Li¹,

Yang²,

Cai³

et al. 2015

Proceedings of the International Conference on Computer Information Systems and Industrial Applications

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show abstract

“…It depends only on the data and automatically adjusts the shrinkage parameter according to the data. A similar GCV method for wavelet thresholding has been proposed in [48]- [50]. Note that, although we are suggesting the use of a GCV function, is also it possible to adapt the new SURE approach [51] for this task.…”

Section: Generalized Cross Validation (Gcv) For Shearlet Threshomentioning

confidence: 99%

Shearlet-Based Deconvolution

Patel

Easley

Healy

2009

IEEE Trans. on Image Process.

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Abstract-In this paper, a new type of deconvolution algorithm is proposed that is based on estimating the image from a shearlet decomposition. Shearlets provide a multidirectional and multiscale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets. Constructions such as curvelets and contourlets share similar properties, yet their implementations are significantly different from that of shearlets. Taking advantage of unique properties of a new M-channel implementation of the shearlet transform, we develop an algorithm that allows for the approximation inversion operator to be controlled on a multiscale and multidirectional basis. A key improvement over closely related approaches such as ForWaRD is the automatic determination of the threshold values for the noise shrinkage for each scale and direction without explicit knowledge of the noise variance using a generalized cross validation (GCV). Various tests show that this method can perform significantly better than many competitive deconvolution algorithms.

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“…Tomasi proposed the bilateral filtering (BF) by introducing the weighted function .The bilateral filtering Retains more image details and continuity compared with the traditional spatial filtering method. The transform domain method process the coefficients in the transform domain, there are many transform domain methods, such as Wavelet transform [2], Shearlet transform [3]and Contourlet transform [4]. Multiresolution Bilateral Filtering (MBF) for Image Denoising which is proposed In the literature [5] combine two popular filters for the two domains, An input image is decomposed into its detail and approximation subbands through wavelet decomposition, bilateral filtering is applied to the approximation subbands, at the same time wavelet Thresholding is applied to the detail subbands, But the two-dimensional wavelet transform can only extract information in two directions.…”

Section: Introductionmentioning

confidence: 99%

Comparative Performance Analysis of Contourlet Transform using Non Subsampled Algorithm in Digital Image Processing

Kourav¹,

Ubs²

2017

International Advanced Research Journal in Science, Engineering

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Abstract:In this paper we have compared Nun Subsampled Contourlet Transform and Wavelet Transform in image denoising on the basis of PSNR, RMSE and SSIM. In order to remove the image noise more effectively an image denoising algorithm based on Non Subsampled Contourlet Transform (NSCT) is proposed. The noisy image is first decomposed into multi-scale and multi-directional subbands by NSCT, and direction subbands of each high-pass component is processed by the new threshold function which is obtained by the Bayes threshold that based on stratified noise estimation. During the reconstruction, the low-pass subband constructed image is further denoisied by the bilateral filtering in the spatial domain. Experimental results compare with the result of image denoising using wavelet transform. It demonstrates that the proposed method can improve de-noising performance.

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Wavelet shrinkage and generalized cross validation for image denoising

Cited by 92 publications

References 13 publications

Image Denoising Algorithm Based on Non-Subsampled Contourlet Transform and Bilateral Filtering

Image Denoising Algorithm Based on Non-Subsampled Contourlet Transform and Bilateral Filtering

Shearlet-Based Deconvolution

Comparative Performance Analysis of Contourlet Transform using Non Subsampled Algorithm in Digital Image Processing

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