Efficient wavelet-based image denoising algorithm

Cai, Zhaohui; Cheng, Tonglei; Lu, Chao; Subramanian, K. R.

doi:10.1049/el:20010466

Cited by 52 publications

(20 citation statements)

References 5 publications

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“…For clarity, only a portion is displayed for each image. For demonstrating the effectiveness of the proposed WMSAD algorithm in noise reduction and edge preservation, the WMSAD algorithm is compared with the counterparts of the robust anisotropic diffusion [4] (RAD), the BSF algorithm [22], and the EWID algorithm [24] in detail from PSNR and the visual quality of the denoised images. The BSF is one of the state-of-the-art wavelet-based denoising techniques while the EWID algorithm is an improved version of the LAWMAP algorithm [19].…”

Section: Resultsmentioning

confidence: 99%

“…Table II. Performance (PSNR in dB) of the proposed WMSAD algorithm compared with that of the RAD [4], BSF [22], EWID [24], Complex_Steer [26], SMG [27], WMSAD-I, and WT_MMSE algorithms for the images of Lena, Barbara, Goldhill, and Peppers with respect to different noise variances. The results of RAD and EWID are from the authors' implementations rather than from the original papers.…”

Section: Discussionmentioning

confidence: 99%

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Wavelet-Based Multiscale Anisotropic Diffusion With Adaptive Statistical Analysis for Image Restoration

Zhong¹,

Sun

2008

IEEE Trans. Circuits Syst. I

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The anisotropic diffusion techniques are in general efficient to preserve image edges when they are used to reduce noise. However, they are not very effective to denoise those images that are corrupted by a high level of noise mainly for the lack of a reliable edge-stopping criterion in the partial differential equation (PDE). In this paper, a new algorithm is developed to tackle this problem. The main contribution of this paper is in the construction of a new regularization method for the PDE by using the overcompleted dyadic wavelet transform (DWT). It proposes to perform anisotropic diffusion in the more stationary DWT domain rather than directly in the raw noisy image domain. In the DWT domain, since noise tends to decrease as the scale increases, at each scale, noise has less influence on the PDE than that in the raw noisy image domain. As a result, the edge-stopping criterion and other partial derivative measurements in the PDE become more reliable. Furthermore, there is no need to do Guassian smoothing or any other smoothing operations. Experiment results show that the proposed algorithm can significantly reduce noise while preserving image edges. IEEEThis work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. AbstractIn general, the anisotropic diffusion techniques are efficient to preserve image edges when they are used for reducing noise. However, they are not very efficient to reduce noise for those images that are corrupted by a high level of noise mainly for the lack of a reliable edge-stopping criterion in the partial differential equation (PDE). In this paper, a new algorithm is developed to address this problem. The contribution of this paper is on the construction of a new regularization method for the PDE using wavelet transform without doing Gaussian smoothing or any other smoothing. As a result, the edge-stopping criterion of the PDE and other gradient measurements are more reliable, making the anisotropic diffusion more efficient for noise reduction. Experimental results have shown that the proposed algorithm can significantly reduce noise while preserving image edges.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Wavelet-Based Multiscale Anisotropic Diffusion With Adaptive Statistical Analysis for Image Restoration

Zhong¹,

Sun

2008

IEEE Trans. Circuits Syst. I

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“…The images are slices and sinogram of 3-D SheppLogan head phantom projection image. Third, for each scale, classify the wavelet coefficients into two categories according to (4). If Subject absent the formula, the wavelet coefficients corresponds to irregular wavelet coefficients.…”

Section: B Wiener Filteringmentioning

confidence: 99%

“…So the wavelet means take a major position on the CBCT image denoising. The classic wavelet algorithm [4] [5] has achieved good results. But most of these techniques are based on threshold or shrinkage so they cannot guarantee to preserve the edges as much as possible.…”

Section: Introductionmentioning

confidence: 99%

CBCT image denoising based on multi-scale wavelet transform

Yin

Wang

et al. 2010

2010 3rd International Conference on Biomedical Engineering and Informatics

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Cone-Beam Computed Tomography (CBCT) has always been in the forefront of medical image processing. The denoising as a image pre-processing, has a great affected on the image analysis and recognition. In this paper, a new algorithm for image denoising was proposed. By thresholding the interscale wavelet coefficient magnitude sum(WCMS) within a cone of influence (COI), the wavelet coefficients are classified into 2 categories: irregular coefficients, and edge-related and regular coefficients. They are processed by different ways. Meanwhile according to the projection image sequences characteristics in CBCT system, an effective noise variance estimated methods was proposed. The experiment shows that our algorithm can improve PSNR form 1.3dB to 2.6dB, and the image border is more clearly.

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“…The order statistics filters are designed to suppress noise of different nature, they can remove impulsive noise and guarantee detail preservation [1][2][3]. On the other hand, the filters based in the wavelet domain provide a better performance in terms of noise suppression in comparison with different space domain filters [4,5].…”

Section: Introductionmentioning

confidence: 99%

Wavelet Order Statistics Filters for Image Denoising

et al. 2009

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This paper presents the wavelet order statistics filters for the removal of impulsive and speckle noise in color image applications. The proposed filtering scheme is defined as two filters in the wavelet domain to conform to the structure of a general filter that can be modified in some headings. The first filter is based on redundancy of approaches and the second one is the wavelet domain iterative center weighted median algorithm. With the structure of the proposed filter different implementations for the estimation of the noisy sample are carried out using different order statistics algorithms that by their good performance can be beneficial in color image processing applications.

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Efficient wavelet-based image denoising algorithm

Cited by 52 publications

References 5 publications

Wavelet-Based Multiscale Anisotropic Diffusion With Adaptive Statistical Analysis for Image Restoration

Wavelet-Based Multiscale Anisotropic Diffusion With Adaptive Statistical Analysis for Image Restoration

CBCT image denoising based on multi-scale wavelet transform

Wavelet Order Statistics Filters for Image Denoising

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