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Alternating Minimization Method for Total Variation Based wavelet shrinkage Model

Communications in Computational Physics 2010年第10期8卷 976-994页

作者： Tieyong Zeng Xiaolong Li Michael Ng Department of Mathematics Hong Kong Baptist UniversityKowloon TongHong KongChina Institute of Computer Science&Technology Peking UniversityBeijing 100871China

In this paper,we introduce a novel hybrid variational model which generalizes the classical total variation method and the wavelet shrinkage *** alternating minimization direction algorithm is then *** also prove that it converges strongly to the minimizer of the proposed hybrid ***,some numerical examples illustrate clearly that the new model outperforms the standard total variation method and wavelet shrinkage method as it recovers better image details and avoids the Gibbs oscillations.

关键词： Alternating minimization convergence Gibbs oscillation wavelet shrinkage total variation

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Outlier detection algorithm for satellite gravity gradiometry data using wavelet shrinkage de-noising

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Geodesy and Geodynamics 2012年第2期3卷 47-52页

作者： Wu Yunlong Li Hui Zou Zhengbo Kang Kaixuan Muhammad Sadiq Key Laboratory of Earthquake Geodesy Institute of Seismology China Earthquake Administration Wuhan 430071 China Crustal Movement Laboratory Wuhan 430071 China Department of Earth Sciences Quaid-i-Azam University Islamabad 45320 Pakistan

On the： basis of wavelet theory, we propose an outlier-detection algorithm for satellite gravity ometry by applying a wavelet-shrinkage-de-noising method to some simulation data with white noise and ers. The result Shows that this novel algorithm has a 97% success rate in outlier identification and that be efficiently used for pre-processing real satellite gravity gradiometry data.

关键词： satellite gravity gradiometry outlier detection wavelet shrinkage threshold Haar wavelet

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An Improved MRI Denoising Algorithm based on wavelet shrinkage

An Improved MRI Denoising Algorithm based on Wavelet Shrinka...

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第26届中国控制与决策会议

作者： Kaikai Song Qiang Ling Zhaohui Li Feng Li University of Science and Technology of China

ISBN: (纸本)9781479937097

Magnetic resonance imaging（MRI） is very important in medical diagnosis. Denoising is a critical step for MRI diagnosis. wavelet shrinkage is an efficient denoising method. It can be further classified into two types, the threshold method and the proportional-shrink method. However, both methods have their disadvantages. When the threshold method is implemented, the noise cannot be perfectly removed under a hard threshold while the denoised image may have fuzzy edges with a soft threshold. Furthermore, when the noise is too strong, the noise removal may not be enough by the threshold method. The proportional-shrink method requires that the variance field of the wavelet coefficients should change smoothly and the noise should obey a Gaussian distribution. If these assumptions are violated, the estimated ratios would not be precise so that too much texture information may be removed and the image can be distorted. This paper presents an improved method to combine the above two methods. By combining the processed results together,the improved method can achieve a good balance between denoising and retaining the texture information. We verify the efficiency of our method through some simulated data from an open database.

关键词： wavelet shrinkage magnetic resonance imaging denoising threshold proportional-shrink

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wavelet inverse scale space for image restoration

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Journal of Systems Engineering and Electronics 2012年第6期23卷 929-935页

作者： Binbin Hao Jianguang Zhu School of Science China University of Petroleum Qingdao 266580 E R. China School of Science Shandong University of Science and Technology Qingdao 266590 R R. China

This paper proposes a model for image restoration by combining the wavelet shrinkage and inverse scale space （ISS） method. The ISS is applied to the wavelet representation to modify the retained wavelet coefficients, and the coefficients smaller than the threshold are set to zero. The curvature term of the ISS can remove the edge artifacts and preserve sharp edges. For the multiscale interpretation of the ISS and the multiscale property of the wavelet representation, small details are preserved. This paper illustrates that the wavelet ISS model can be deduced from the wavelet based on a total variation minimization problem. A stopping criterion is obtained from this minimization in the sense of the Bregman distance in the wavelet domain. Numerical examples show the improvement for the image denoising with the proposed method in the sense of the signal to noise ratio and with fewer details remained in the residue.

关键词： wavelet shrinkage inverse scale space (ISS) curva-ture term total variation minimization image restoration.

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A NEW APPROACH FOR UNSUPERVISED RESTORING IMAGES BASED ON wavelet-DOMAIN PROJECTION PURSUIT LEARNING NETWORK

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Journal of Electronics(China) 2003年第5期20卷 383-386页

作者： LinWei TianZheng WenXianbin NorthwesternPolytechnicUniversity Xi‘an710072 NationalLaboratoryofPatternRecognition Beiiing100080 KeyLabofEducationMinistryforImageProcessingandIntelligentControl Wuhan430074

The wavelet-Domain Projection Pursuit Learning Network (WDPPLN) is proposedfor restoring degraded image. The new network combines the advantages of both projectionpursuit and wavelet shrinkage. Restoring image is very difficult when little is known about apriori knowledge for multisource degraded factors. WDPPLN successfully resolves this problemby separately processing wavelet coefficients and scale coefficients. Parameters in WDPPLN,which are used to simulate degraded factors, are estimated via WDPPLN training, using scalecoefficients. Also, WDPPLN uses soft-threshold of wavelet shrinkage technique to suppress noisein three high frequency subbands. The new method is compared with the traditional methodsand the Projection Pursuit Learning Network (PPLN) method. Experimental results demonstratethat it is an effective method for unsupervised restoring degraded image.

关键词： wavelet-domain Projection pursuit learning network wavelet shrinkage Unsu-pervised restoring image

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